An Analysis and Design of Frozen Shrimp Traceability System Based on Digital Business Ecosystem

AN ANALYSIS AND DESIGN OF FROZEN SHRIMP
TRACEABILITY SYSTEM BASED ON
DIGITAL BUSINESS ECOSYSTEM
ADITIA GINANTAKA
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
DECLARATION OF ORIGINALITY
AND COPYRIGHT TRANSFER *
I hereby declare that thesis entitled An Analysis and Design of Frozen Shrimp
Traceability System Based on Digital Business Ecosystem is my own work and to
the best of my knowledge it contains no material previously published in any
university. All of incorporated originated from other published as well as
unpublished papers are stated clearly in the text as well as in the references.
Hereby, I state that the copyright to this paper is transferred to Bogor
Agriculture University.
Bogor, April 2015
Aditia Ginantaka
F351130361
SUMMARY
ADITIA GINANTAKA. An Analysis and Design of Frozen Shrimp Traceability
System Based on Digital Business Ecosystem. Supervised by TAUFIK DJATNA
and IRVAN FAIZAL.
Traceability is the ability to verify the history and location of a food product,
thus we could get information on each supply chain actor, who the immediate
supplier is and to whom the product sent. Therefore, system approach could used
to manage and integrated all information by collecting, store and then retrieve data
and information about the product from the earlier stages of production process.
One of the biggest challenges is how to exchange and keep the flow of information
in a standardized format between supply chain actors. Therefore, to solve the
problem, this research focuses on developing a model for documenting and
exchanged information based on the digital business ecosystem (DBE). Besides,
DBE would support the supply chain actor, in order to integrate all information
including the quality and product safety. That’s why DBE is promises as a
foundation to establish traceability system.
The objective of this work were to analyze the requirement and to design of
traceability system. This research focuses to the proposed system for frozen
Vanname shrimp products and then verify and validate the traceability system to
evaluate system performance. Business process model notation (BPMN) was the
primary tool in analyzing task for capturing and transferring data processing
between traceable units. BPMN diagram was construct based on interaction
between the supply chain actors in it where each actor has their roles to achieve a
common goal.
The results of the analysis showcased how traceability system work in DBE
which involved on dispersed stakeholders. Manual data transformation to the digital
system was provided by stakeholders using digital species metaphors, which has
been performed and implemented in Java language program. The most appropriate
attributes to capture were chosen with Relief method. Water temperature has been
selected as attribute which have to keep recorded, to ensure that temperature kept
maintained on the entire supply chain stages. This system could claim that the
product were safe using cosine similarity computation. As first response to the
customers, traceability system also developed to provides information about time
required for completion issue after source of product defect has traced. Thus, Fuzzy
Associative Memory (FAM) method was used to predict handling time, which
assumed influenced by the amount of products inventory that used to replace
product defect, amount of products that have to recall from market and amount of
time spends for handling inspection process internally. Inspection based on white
box verification method was used to proven whether the logic of the model in each
stakeholders is implemented correctly or not. Validation has performed using user
interview method and simulation test based on black box principle. Result of
documentation all evaluation stages, show that traceability system was verified by
checking each performance and formulation.
Keywords: traceability, digital business ecosystem, food safety
RINGKASAN
ADITIA GINANTAKA. Analisis dan Desain Sistem Traceability Produk Udang
Beku Berbasis Digital Business Ecosystem. Dibimbing oleh TAUFIK DJATNA
dan IRVAN FAIZAL.
Traceability merupakan kemampuan memeriksa riwayat dan lokasi sebuah
produk pangan, sehingga diperoleh informasi berkaitan dengan siapa pemasok dan
kemana produk didistribusikan pada jaringan rantai pasoknya. Pendekatan sistem
digunakan untuk mengatur dan mengintegrasikan informasi melalui
pendokumentasian data pada setiap titik rantai pasok dan rantai proses penanganan
produk. Salah satu tantangan besar adalah, bagaimana melakukan pertukaran dan
menjaga aliran informasi dalam format yang standar diantara pelaku rantai pasok.
Sehingga, penelitian ini fokus pada pengembangan model sistem untuk proses
dokumentasi dan transfer informasi berbasis pada konsep digital business
ecosystem (DBE).
Penelitian ini bertujuan untuk menganalisis kebutuhan serta mendesain
sistem traceability. Fokus penelitian ini adalah untuk menawarkan gagasan sebuah
sistem traceability produk udang beku, kemudian melakukan verifikasi dan validasi
sistem untuk mengevaluasi kinerja sistem. Business process model and notation
(BPMN) merupakan alat utama untuk analisis tugas-tugas dalam proses
pendokumentasian dan transfer data diantara stakeholder. Diagram BPMN dibuat
berdasarkan interaksi di antara pelaku rantai pasok yang ada di dalamnya, dimana
setiap aktor memiliki peran masing-masing untuk mencapai tujuan bersama.
Hasil analisis menunjukan bahwa sistem traceability berbasis DBE ini
melibatkan lima stakeholder. Proses transfer data ke dalam bentuk digital dilakukan
oleh setiap stakeholder menggunakan aplikasi digital yang merupakan
perumpamaan spesies dalam ekosistem digital (digital spesies). Spesies digital
didesain dan dikembangkan dengan menggunakan bahasa pemrograman Java.
Atribut data yang harus selalu dokumentasikan ditetapkan dengan menggunakan
metode Relief. Suhu air dan komoditas ikan ditentukan sebagai atribut yang harus
selalu direkam selama proses produksi. Sistem ini dapat menegaskan keamanan
produk menggunakan teknik komputasi Cosine Similarity. Jumlah waktu yang
dibutuhkan untuk penanganan produk yang cacat, dapat diprediksi menggunakan
metode Fuzzy Associative Memory (FAM). Diasumsikan bahwa input sistem FAM,
dipengaruhi oleh variable jumlah persediaan produk, jumlah produk recall dan
jumlah waktu yang dibutuhkan untuk melakukan inspeksi lapang pada unit-unit
penganangan produk. Verifikasi sistem dengan melakukan inspeksi berbasis
metode white box digunakan untuk membuktikan apakah kerangka logis dari proses
pemrograman sistem berfungsi secara benar pada setiap stakeholder. Proses
validasi dilakukan dengan menggunakan metode interview dan simulasi berbasis
metode black box. Hasil pengujian menunjukan bahwa sistem telah siap untuk
digunakan dalam dunia nyata.
Kata kunci: traceability, digital business ecosystem, keamanan pangan
© Copyright 2015 by IPB
All Rights Reserved
No part or all of this thesis may be excerpted without or mentioning the sources.
Excerption only for research and education use, writing for scientific papers,
reporting, critical writing or reviewing of a problem. Excerption doesn’t inflict a
financial loss in the paper interest of IPB.
No part or all part of this thesis may be transmitted and reproduced in any forms
without a written permission from IPB.
AN ANALYSIS AND DESIGN OF FROZEN SHRIMP
TRACEABILITY SYSTEM BASED ON
DIGITAL BUSINESS ECOSYSTEM
ADITIA GINANTAKA
Thesis
as partial fulfillment of the requirements
for the degree of Master of Science
in the Agroindustrial Technology Study Program
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015
Non-committee examiner: Prof Dr Ir Kudang Boro Seminar, MSc
Thesis Title : An Analysis and Design of Frozen Shrimp Traceability System
Based on Digital Business Ecosystem
Name
: Aditia Ginantaka
NIM
: F351130361
Approved by
Supervisor
Dr Irvan Faizal, MEng
Member
Dr Eng Taufik Djatna, STP, MSi
Chairman
Acknowledged by
Head of
Agroindustrial Technology
Study Program
Dean of Graduate School
Prof Dr Ir Machfud, MS
Dr Ir Dahrul Syah, MScAgr
Examination date: 14th April 2015
Passed date:
PREFACE
Praise to Allah Subhanahu Wa Ta’ala the Almighty for the overall conducive
conditions for me to pursue my study and thesis work in Graduate School of Bogor
Agricultural University, Indonesia and His blessings to successfully complete it.
Firstly, I would like to express my sincere appreciation to Dr Eng Taufik
Djatna, STP, MSi and Dr Irvan Faizal, MEng as supervisor for the support and
encouragement during my study in Bogor Agricultural University. I am also
indebted to Prof Dr Ir Kudang Boro Seminar, MSc as Non-committee Examiner for
his constructive comments on this thesis.
I would like to thank PT Nusa Ayu Karamba for giving me the opportunity to
conduct my research and data collection. I wish to thank all lecturers and
colleagues, especially TIP 2013, at the Agroindustrial Technology Study Program
for cooperation and shared their valuable ideas and insights in relation to this study.
It has been a pleasure to work with you.
Last but not least, I want to express my deepest appreciation to my parents
who have always prayed for me and gave me moral support to complete my studies
and I am grateful to my wife for their true and endless love, for never-failing
patience and encouragement.
I wish that this work will be of benefit to readers and contribute to the
development of knowledge.
Bogor, April 2015
Aditia Ginantaka
TABLE OF CONTENTS
TABLE OF CONTENTS
vi
LIST OF TABLES
vii
LIST OF FIGURES
vii
LIST OF APPENDIXES
vii
1 INTRODUCTION
Background
Problem Definition
Research Objectives
Boundaries of Research
1
1
3
3
3
2 LITERATURE REVIEW
Traceability
Traceability System in Fisheries Supply Chain
Digital Business Ecosystem
System Analysis and Design
Data Mining and Soft Computing
PT Nusa Ayu Karamba
4
4
5
7
8
12
14
3 METHODOLOGY
Framework
Business Process Analysis
Identification of System Component
Determine Critical Attribute
Design Traceability Information System
Verification and validation
14
14
15
16
16
17
20
4 RESULTS AND DISCUSSIONS
Identification of Existing Business Process
Requirement Analysis
Design Traceability Information System
System Evaluation
21
21
23
27
31
5 CONCLUSIONS AND RECOMENDATIONS
Conclusions
Recomendations
33
33
34
REFERENCES
34
APPENDIXES
37
BIOGRAPHY
58
LIST OF TABLES
1
2
3
4
5
6
7
8
Notation used in developing the BPMN (White and Miers 2008)
Data identification and range of value processing parameters
Coding required and existing coding system
Seed data documentation result on breeding farm
Results of factor analysis with Relief method
Shrimp data documentation result from ongrowing unit
Shrimp data documentation result from processing unit
Standard data attribute of quality reference
11
23
23
24
27
29
29
30
LIST OF FIGURES
1 Linking database in a traceability system (Adopted from Furness and
Osman 2006)
5
2 An architecture of the traceability system in fisheries product (Adopted
from Parreno-Marchante et al. 2013)
Analytical System Entity Construct (Wasson 2006)
System development life cycle (Kendall and Kendall 2011)
Sample BPMN process (Derreck and Miers 2008)
The framework of traceability system for frozen shrimp product
Triangular membership function of Fuzzy set X for variable A
Current business process and provision of information at the company
(Adopted from Parenno-Marchante et al. 2014)
9 Breeding unit
10 Ongrowing unit
11 Fragment of seeds data documentation process
12 Component for traceability system (Wasson 2006)
13 Structural coupling between supply chain ecosystem and digital ecosystem
in traceability system (Nachira et al. 2007)
14 Application interface of seed data input
15 Use of digital device in traceability system
16 Possible information exchange between different actors in the frozen
shrimp supply chain (Adopted from Thakur and Hurburgh 2009)
17 (a) Inputting data process; (b) Database interface on traceability system
18 (a) Scan the barcode using barcode scanner; (b) Choosing the seed ID
manually
19 Result of retrieval data process
3
4
5
6
7
8
6
9
10
10
15
19
21
22
22
25
26
26
27
28
29
32
32
33
LIST OF APPENDIXES
1
2
3
4
5
6
7
8
Questionnair TU1
Documentation result from software application
Fuzzy set formulation for Product Inventory
Fuzzy set formulation of product recall
Time required to perform several inspection process
Fuzzy set formulation for inspection time
Fuzzy set formulation for total handling
FAM rules of prediction handling time
37
41
42
43
44
45
46
47
9 Computation result of matrix M and B
10 Requirement verification matrix (RVM)
11 Sample of application form for data capturing on breeding unit
12 Fragment of documentation process at ongrowing unit
13 Fragment of documentation process at processing unit
14 Fragment of documentation process at cold storage unit
15 Fragment of documentation process at retailer unit
48
50
53
54
55
56
57
1
1 INTRODUCTION
Background
Traceability is an ability to provide the information of history and location
based on movement of goods in every stage of production and distribution process.
The system requires the supply chain actors knowing who the immediate supplier
is and to whom the product sent, that each actor have the information access, both
to upstream and to downstream (Bosona and Gebresenbet 2013; Mgonja et al. 2013).
Some countries require the producers to have traceability system as an effort to
protect their people’s health and safety. Thus, this system is very important for the
exporters to avoid rejection from importer countries. Several laws and legislation
that regulate the food safety in some countries are Bioterrorism Act by the
government of United States of America in 2002 (Thakur and Hurburgh 2010),
European Union’s General Food Law which was published in 2005 and Chinese
Food Safety Law which had been implemented in 2009 (Hu et al. 2013).
Traceability system could reduce cost and labour related the information
exchange among business partners and also in information and data logistics
improvement of the company internally. Besides that, traceability system provides
access to more accurate and more timely information needed in decision making
process about how and what to produce, and makes the company has a competitive
advantage through its ability in documenting products’ information (Olsen and
Borit 2013). Customers is also very interested to receive more accurate information
about food product and willing to pay more for food product that could provide
service to consult about the origin and freshness declared by using traceability
system. The benefit of this system can be the reason for the company to implement
traceability system which not only pushed by the compliance to the regulation in
some importer countries.
However, every business actor in supply chain must collecting the needed
information together internally (internal traceability), continuously (recordkeeping), and integrating them to supports the improvement of traceability chain
system among the suppliers. That is why, the improvement of traceability system
needs a technological innovation that can support the process of products
identification, information collection, data storage and transformation, and system
integration. Some researches were done to build the structure of information,
sending and receiving information from various actors in the system. Process of
standardizing the information and automation process in data identification,
measurement, and storage, are very needed (Thakur et al. 2011).
In other side, one of big challenge in improving the traceability system is
about how to exchange and to provide data among the suppliers in standard format
(Thakur et al. 2010; Hu et al. 2013). Thus, traceability system needs to use
information technology. The growth of information technology has changed the
documentation from paper-based into digital. Documentation process with digitalbased is able to build an information documentation precisely and effectively, that
the improvement of traceability system based on digital business ecosystem (DBE)
is highly needed. DBE is a representation of a business ecosystem where the
business actors interact in digital environment (Nachira et al. 2007). Like in natural
2
environment, every supplier in digital traceability system can be assumed as a
species in digital ecosystem which interacts in documenting and acquiring
information.
Traceability system based on digital infrastructure have been develop in order to
record-keeping necessary information on tracking and tracing process and for
automatically deliver information to customers. However, there are differences in
the information provided in each agricultural commodity. Thus, producers have to
choose the necessary information that customers really want to know, as well as the
customer's right to know. Several researchers have been proposed of the electronic
chain traceability system, such as in vegetables supply chain (Hu et al. 2013), on
soya beans (Thakur and Donelly 2010) and in aquaculture products (ParrenoMarchante et al. 2014).
Food product is a perishable goods and have several supply chain actors. They
are started by the production of raw materials from farmer, wholesaler, processor,
distributor, and retailer. These characteristics requires appropriate hold and control
to keep the quality and safety. Increasing complexity of food supply chain has
encourage the supply chain actors to make a vertical integration for information
exchange. Therefore, traceability system based on DBE aims to construct a digital
environment that provide and facilitate stakeholders in sharing and acquisition
information. Each stakeholders could interact easily in digital environment.
Therefore, DBE concept could use as a foundation to establish traceability system.
As one of main commodities in fisheries with 162.068 tons of export volume
in 2012 (KKP 2013), shrimps are potentially to be one of main income for the
business owners in Indonesia. Thus, the improvement of traceability system is
required to raise the trust of importers to Indonesian frozen shrimps. Ministry of
Marine Affairs and Fisheries of the Republic of Indonesia also has regulate about
the obligation of traceability system implementation in Ministerial Decree No KEP.
01/MEN/2007 (KKP 2007). Traceability system for fishery products was done on
paper documentation, in the first appearance in 2000, and in 2008 this system was
suggested to be automatically implemented (Parreno-Marchante et al. 2014). DBE
has a big potentiality in helping SMEs to connect each others in order to exchange
and acquire data and information between supply chain actors. Besides, tracebility
based on DBE have to support the supply chain actor, in order to integrate all
information including the quality and product safety. Thus, this system needs
supported with the capability to estimates that whole production processes were in
standard procedures. Therefore we have to measure the similarity between field
data and the standard value of each data.
Further orientation in establishing this system is that traceability is a company
responsibility effort to serve customer complaints if there are several incident
occurs after consume the fish product. As responsible action, the company should
handling whole issue related to food safety incident, such as identify the cause of
incident, recall suspect product from the market, provided information to the food
inspection authorities etc. Therefore, the company should give a first response to
customers about how long the problem could be resolved. This system would
develop using a method for predict total handling time. Fuzzy associative memory
(FAM) (Kosko, 1990) was chosen because this method could translates the
structured linguistic condition into numerical framework and provide rule
3
association from historical condition of several factors that influence time to
handling issue, thus the prediction could be adaptively inferred and modified.
Problem Definition
Improvement of digital technology in the implementation of internal
traceability system has produced some advantages which have more significant
relationship to efficiency of time and human resource (Scheer 2006). Several of
technology have been used in data documentation, such as PDA (personal digital
assistant) with GPRS-based and 2D barcode in meats labeling (Ben-hai et al. 2010),
traceability system with 2D barcode and RFID (radio frequency identification) in
wheat flours (Qian et al. 2011), RFID and infrastructure with WSN-based (wireless
sensor networks) for fishery products (Parreno-Marchante et al. 2014).
Based on the last development in traceability system, digital-based
technology is needed to improve traceability system in Indonesia. There is still no
any fishery business in Indonesia using integrated traceability software on each
supply chain actors. Both small and medium enterprises are still using paper-based
system. Therefore, it is needed to improve integrated digital traceability information
system based on digital business ecosystem (DBE) concept as a model in chain
traceability.
Research Objectives
According the motivation that have been delivered, the objective of this work
were (1) to analyze the requirement of traceability system by means of business
process analysis; (2) to design component, rule, role and integration for traceability
management information system; and (3) to verify and validate the traceability
system to evaluate system performance.
Boundaries of Research
Traceability system was implemented for frozen Vanname shrimp.
Analyzed ecosystem was the internal ecosystem of company at product unit which
represents the supply chain system such as, breeding unit, ongrowing unit,
processing unit, cold storage unit, and retailer unit. Research object had
implemented pond coding and data documenting manually with data compilation
from production parameter including the amount of seeds, temperature, pH, feed,
and so forth but not yet integrated.
Focus of the research is to analyze the need for a management information
system through data collection, storage, data exchanging and retrieve data using a
digital infrastructure. System design is intended to be used by supply chain actors,
whereby the same supply chain actor level performs the same role, thus forming a
digital community to perform data collection and interact with other supply chain
actor communities to transfer data in a digital environment. Interaction between
communities in digital environment would be form a digital ecosystem that aims to
provide all the information products to consumers through retailers.
4
Each product handling unit is the stakeholders in the system. System design
focused on documenting product history at every stakeholder and tracing at retailer
unit. This system can only be used by supply chain actors who have adopted ICT
tools in their business activities, for instance the use of personal computers (PC),
then connectivity between computers by P2P (peer-to-peer) networks, the local area
network (LAN), or enabling the internet connections. System design included
software development for data input and data query with login system according to
stakeholder in digital ecosystem. System capability was developed for similarity
measurement and prediction of total handling time using the method of Fuzzy
Associative Memory (FAM) (Kosko 1990). This system produced report with
needed information for stakeholders, and last but not least traceability system was
evaluate.
2 LITERATURE REVIEW
Traceability
The general concept of traceability can be defined as an ability to identify
the origin of goods or product based on recording information at the entire pathway
of supply chain. However, various definitions have been derived for traceability,
including a European Union (EU) General Food Law Regulation definition in
which traceability is defined as “the ability to trace and follow a food, feed, foodproducing animal or substance through all stages of production and distribution”.
An International Standards Organisation (ISO) definition is also to be found that
defines traceability as “the ability to trace the history, application or location of an
entity by meansof recorded information” (Furness and Osman 2006). This is often
termed the principle of “one-up and one-down” (Hu et al. 2013; Thakur and
Hurburgh 2010).
The increasing demand a high-quality food and feed products is driven by
consumer experience with food safety and health issues. Therefore, there are
increasing of interest in developing a system that aims to food traceability efforts
(Thakur and Hurburgh 2009). The UK Food Agency define functional roles for
traceability on the food supply chain management, such as (1) to facilitate rapid
response to solve food safety incidents, (2) to facilitate sampling food at critical
points at the entire food supply chain mechanism, (3) provide access to gain
information concerning foods or food ingredients that could support to food safety,
(4) to help determine supply chain integrity with respect to food claims and false
labelling (5) to prevent fraud in the food trade, (6) to support food distribution
improvement processes and minimize wastage of food, (7) to support food hygiene
in processing and handling of food (Furness and Osman 2006).
There are two categories of traceability that are commonly used on several
company. The first is internal traceability and then external traceability. Internal
traceability related the the ability to identify and follow a product within a single
company or factory. Meanwhile, external traceability which relates to product
information that a company either receives or provides to other supply chain actor.
The difference between the both of categories is the scope of stakeholders and
anyone who take a role to provide and receive informations. Meanwhile the
5
similarity in both categories traceability system is, concern only to the ability to
trace goods, by identify the specific product and linked to the related records.
However it is does not mean that all the information should be permanently visible
by being included on a product label.
Thakur and Donnelly (2010) explains that the implementation of a traceability
system requires an analysis of the product material flow, the flow of information
and the information handling. There are three categories of information that needs
to be captured by each supply chain actor, for instance the product information,
process information, and quality information. To allow access to the information
that have documented traceability system requires a network infrastructure
(including use of the Internet) with appropriately authorised access control and
communication protocols as shown on Figure 1.
Figure 1 Linking database in a traceability system
(Adopted from Furness and Osman 2006)
Traceability system is necessary to use item-attendant identifiers, to support
identification of specific information. The most probably identification technique
using standard EAN UCC (European Article Numbering Universal Code Council)
which is an association of international numbering. EAN UCC provides system of
numbering and identification using the Global Trade Item Number (GTIN) as
identifiers of the type of goods on trading transaction (Furnes and Osman 2006).
Traceability System in Fisheries Supply Chain
Fisheries sector has become one of the food-producing sector of the fastest
growing, especially in the Aquaculture subsector. The appropriate management
could be a key for supporting the role to meet the rising demand for fishery products.
(FAO 2014). Thus, several country have implement traceability system especialy
to record environmental paramenters which must be controlled and strickly
maintained such as temperature and humidity in the processing environment, during
transport or warehousing.
The pilot project of traceability system has been deployment in two SMEs in
Spain and Slovenia. The system was design into four main component. The first
component consist of sensors and data input devices, such as fixed or hand-held
6
RFID readers, antennas, tags and barcode readers. The second component is the set
of capture and query software that could transfer data into database or traceability
repository. The third component is the traceability database to store the traceability
data generated during the product handling operations. The fourth component is the
website which service customer to gain the product information by using a web
browser or a mobile application (Parreno-Marchante et al. 2013). For shrimp
traceability system development, the architecture was adopted as shown on Figure
2.
Aquatic products have characteristic in complexity and the coexistence of
large and small and high-value and low-value products. In China, a traceability
system was constructed using an anti-counterfeit code for aquatic product
identification. To participate in a traceability system platform, enterprises are
required to use a unified anti-counterfeit code encoding method and a product label
to identify their products and to ensure the benefit and brand of these enterprise
members (Sun et al. 2014).
Database
Query data
application
Barcode
scanner
Customer
Input data
application
Barcode
scanner
Application data
record form
Figure 2 An architecture of the traceability system in
fisheries product (Adopted from ParrenoMarchante et al. 2013)
The largest value contribution of exports Indonesian fishery products are from
shrimp and from group of fish tuna, little tuna and skipjack (KKP 2013). Therefore,
it is important to implement a traceability system on shrimp commodity to achieve
greater sales value of shrimp.
The uniqueness unit of a product that identified at the supply chain is called
traceable unit. For example, at fisheries supply chain, boat and cage could used to
define traceable unit, meanwhile in fish feed, big sack and silo usually used to
define the granularity of traceable unit. Aquatic production batch is also defined as
the traceable unit that aquatic products was caught from the same pond with the
same day. (Karlsen et al. 2011). The term of traceable unit also refer to size or lot
that could be physically and individually identified and that provides the true basis
of an effective system for managing emergencies and attributing responsibilities.
The unique identifiers makes product possible to identified based on the units that
have undergone a given production process so that they can be separated if any
quality or food safety problems (Bennet 2006).
To evaluate traceability system performance, fish processing companies have
to develop their own diagnostic instrument to help them assess their strengths and
weaknesses, and also to attain higher control of food safety problems. The
diagnostic instrument is composed of five main parts, they are (1) contextual factors,
7
(2) traceability system design, (3) traceability system execution, (4) traceability
system requirements, and (5) traceability system performance, and food safety level.
Contextual factors is assumed as complexity of traceability system. There are,
three indicators derived, they are (1) risk level of raw materials for safety, (2) degree
of diversity of raw materials such as many species of fish, and (3) spoilage rate of
raw materials. Traceability system design, related to several factors that compose
traceability system such as, type of identification, mode of data registration,
location of data storage, mode of information communication and the degree of data
standardization. Meanwhile, system execution related to constant interaction
between employees and management involve communication of traceability
procedures and instructions to attain the accuracy of documentation process. For
the last, the effectiveness of traceability system basically supported by determine
the information that needs to trace. Thus, performance of the system can also be
checked on the capability to provide information, the reliability, rapidity, and
precision/accuracy of information.
Digital Business Ecosystem
Historical development of the concept of digital business ecosystem (DBE)
driven by effort to provide favourable environment for SMEs Business and their
networking. Individual businesses can not thrive alone, and must develop in clusters
or economic ecosystems. Thus, the integrated approach for introducing DBE
stressed to the creation of an environment, a business ecosystem, and the need for
IT skills.
DBE constructed by adding digital term in front of business ecosystem term,
which means interaction between business actors in digital environment.
Decomposition of meaning in each term, is as follows:
Digital: the technical infrastructure, based on software technology that
could connect several digital devices directly. This infrastructure could
transports, finds, and connects services and information over Internet
links enabling networked transactions, and distribution of all digital
material within the infrastructure. In other meaning, the infrastructure is
an organism of digital world
Business: An economic community that enabling organizations and
individuals interact each other, in order to produces goods and services of
value to customers, who themselves are members of the ecosystem.
Organization or individual is an organism of business world.
Ecosystem: a biological metaphor that depict the interdependence of all
actors in the business environment, who mutual develop their capabilities
and roles.
Thus, digital business ecosystem is an isomorphic model of biological
behaviour that represented by the software behaviour. Therefore the ICT
infrastructure is designed to support economic activities, which contains the
socially-constructed representations of the business ecosystem. The digital
ecosystem provides representations of the business ecosystem, which are used for
search and discovery, for aggregating and recommending services, for reorganising
value chains, and for recommending potentially cooperating business partners
(Nachira et al. 2007)
8
Digital ecosystem is a digital environment that consists of digital species (DS)
which is analogous to biological species and usually form communities.The
majority of DS consist of hardware together with its associated software. The
hardware is analogous to the body of biological species whereas the software is
analogous to the life of biological species. In nature, a body without life is dead.
Similarly, hardware without any application running on it is useless (Hadzic et al.
2007; Hadzic and Dillon 2008).
The concept of DE has been developed specifically for the health domain
which called DHES (digital health ecosystem). In a DHES, such information may
be a personalized medical record, money transactions between patient and chemist
when purchasing prescribed medication, which is transported within the DHES for
various reasons (Hadzic and Dillon 2008). Every members in DHES could interact
each other using digital health species (DES). Besides, DE also applied in form of
medical records digital ecosystem (MRDES) that enables efficient use of medical
records for the purpose of correct patient identification, diagnosis, appointments
scheduling and the like, in everyday life as well as in emergencysituations. Medical
records digital environment (MRDE) is populated by interconnected medical
records digital components (MRDC) (Hadzic et al. 2007). Meanwhile, DBE has a
big potentiality in helping SMEs to connect with potential customers both in
business-to-business transaction and in business-to-customers transaction (Leon
2007). Based on the great function in several practices, DBE concept have to
establishes by using digital divice as a digital species that would perform the role
of business actor.
Digital business ecosystem reveals the opportunities to enhance the
productivity and efficiency of each business services (Pranata and Skinner 2009).
The following services are needed in DBE such as, payment, business contract and
negotoations, information carriers, billing, trust, reputation and legal compatibility
(Ferronato 2007). Methodology for the design of DBE that consists of the following
five steps, they are (1) identify several types of digital species (DS) based on their
roles, (2) develop intelligent capability of DS, (3) define DS collaborations, (4)
enable, improve and/or construct individual DS and the last is (5) protect the DBE
by implementing security requirements (Hadzic and Dillon 2008). Along with the
advancement of DE technology, security has emerged as a vital element in
protecting the resources and information for the interacting DE member entities in
particular (Pranata and Skinner 2009).
System Analysis and Design
System is an integrated set of interoperable elements, which is have specific
and bounded capabilities explicitly, perform value-added processing by working
synergistically that enable user satisfaction based on their mission-oriented
operational needs in a prescribed operating environment with a specified outcome
and probability of success. Different authors have their own definitions of a system
which is tempered by their personal knowledge and experiences. However, several
standards organizations have achieved convergence and consensus about definition
of a system. From several system example that have been analyzed, there are a
conclusion that a system could produce combinations of products, by-products, or
services.
9
A system entities are described symbolically using a rectangular box as
shown in Figure 3. As an abstraction system composed by inputs that are fed into a
system then processed into an output. For more detail, system entities include
desirable/undesirable inputs, stakeholders, and desirable/undesirable outputs, roles,
resources and control. For more detail, system entities include desirable/undesirable
inputs, stakeholders, and desirable/undesirable outputs, roles, resources and control.
The objective of system analysis and design is seek to know the detail of user
requirement by analyze data input or data flow systematically, process or transform
data, store data, and output information in the context of a particular organization
or enterprise. By doing through analysis, system analysts seek to identify and solve
the right problems. Furthermore, systems analysis and design is used to analyze,
design, and implement improvements in the support of users and the functioning of
businesses that can be accomplished through the use of computerized information
systems.
System bound and environment
Stakeholder
Role, rule, mission,
objective
Acceptable
input
Unacceptable
input
Threat
Acceptable
output
System entity
• Attribute
• Services
• Product
•Performance
• By-products
Opportunity
Unacceptable
output
Resource
Figure 3 Analytical System Entity Construct (Wasson 2006)
The systematic approach take to the analysis and design of information
systems is embodied in what is called the systems development life cycle (SDLC).
The SDLC is a phased approach to analysis and design that holds that systems are
best developed through the use of a specific cycle of analyst and user activities.
There are several opinions about the stages contained in SDLC. However, analysts
generally agreed about organized approach which divided the cycle into seven
phases, as shown in Figure 4. Although each phase is presented discretely, it is
never accomplished as a separate stages. Several activities could occur
simultaneously, and activities may be repeated (Kendall and Kendall 2011).
Requirement analysis on SDLC perform by using several tools method.
BPMN is graphical notation to depict the sequence of process in business activities
that collaborating and interacting to achieve a goal. Business process modeling
constructed to aid a communication with work colleagues inside the organization,
helping them form a shared understanding. Besides, BPMN also used to drive the
way in which work happens in the modern organization and carry the instructions
for how work should happen, who should do it, escalation conditions if it is not
10
done in time, links to other systems etc. BPMN uses a set of specialized graphical
elements to depict a process and how it is performed, as shown on Figure 5.
Figure 4 System development life cycle (Kendall and
Kendall 2011)
Figure 5 Sample BPMN process (White and Miers 2008)
BPMN provides a standard way of representing business processes using
several notation for both high-level descriptive purposes and for detailed. The
notation was agreed as a single notation (representation) that other tools and users
might adopt. With BPMN, only the processes are modeled which could represent
how a business pursues its overarching objectives. However, the objectives are not
captured in the BPMN notation. In developing BPMN, there were different levels
of process modeling, they are (1) process map that is a flow diagram without a lot
of detail other than the names of the activities and perhaps several decision
conditions, (2) process descriptions that provide more extensive information on the
process, such as the people involved in performing the process (roles), the data,
information and so forth, (3) process models are detailed flow-charts encompassing
sufficient information such that the process is amenable to analysis and simulation.
The main notation of a BPMN can be seen on Table 1.
11
Table 1 Notation used in developing the BPMN (White and Miers 2008)
No
1
2
3
4
5
6
Notation
Function
Start Event-Representing the place where a Process
can begin. There are different types of Start Events
according to the actual condition.
Task/ Activities. Representing the steps of working
activity in a business process. This notation is usually
require some type of input, and will usually produce
some sort of output.
Gateway. Showing about how the Process diverges or
converges. This notation separates or connects a process
through sequence flow.
Connectors. Connecting two objects on diagram of
BPMN. Several types of connectors are sequence flow
which shows the order of object flow in a process of
activity, gateway, or event. Then, the message flow
which shows the communication flow between two
participants or system entity, and association which is
used to connect an object with artifact (data or
information source).
End event. Showing that a process or part of a process
is stated finish. Just like start Event, there are several
types of notations of End Event which shows the
differences of the categories as the result of a process.
Artifacts (data object). Used to illustrate mechanism
to capture of additional information from a process
through flow-chart structure. This information has no
direct effect to the characteristic of a process. In the
development of BPMN, the type of data object is
commonly used.
Performance of the system can also be checked on its capability, reliability,
rapidity, and precision/accuracy. Capability is the ability of retrieving the
information required without any error and maybe determined by the reliability of
thetools, procedures, and information sources used. Rapidity refers to speed of
responding to information requests regarding the trade items. Rapidity may be
determined by the information management, tools used, and their automation as
well as the level of cooperation between the supply chain partners.
Precision/accuracy is the ability to pinpoint a particular food product’s movement.
Precision/accuracy maybe determined by consistence of batch sizes used in the
supply chain (EAN.UCC 2003).
Verification and validation of the system could be performed by applying
several test techniques. Software testing is the procedure of executing a program or
system with the intent of finding faults. Software testing is a significant activity of
SDLC. It helps in developing the confidence of a developer that a program does
what it is intended to do so. Black box testing is often used for validation and white
box testing is often used for verification. Black Box Testing is testing based on the
12
requirements specifications and there is no need to examining the code in black box
testing. This is purely done based on customers view point only tester knows the
set of inputs and predictable outputs, meanwhile white box is a test the internal
functioning of the software from the developer’s perspective, white box testing
mainly focus on internal logic and structure of the code. White-box is done when
the programmer has techniques full knowledge on the program structure (Nidhra
and Dondeti 2012).
Data Mining and Soft Computing
Data mining is the process of discovering interesting patterns and knowledge
from enormous amounts of data that collected from several source. The data sources
can include databases, data warehouses, theWeb, other information repositories, or
data that are streamed into the system dynamically. As a result of the natural
evolution of information technology, data mining process consist of several tools
and technique that could use to bridging gap between data and valuable knowledge
that embedded in the vast amount of data. As analogy by refer to the mining of gold
from rocks or sand, we say gold mining instead of rock or sand mining. Thus,
similar meaning to data mining for example, knowledge mining from data,
knowledge extraction, data/pattern analysis, data archaeology, and data dredging.
To discover information from large amount of data we have to perform an
iterative sequence steps. The first is data preprocessing, where data are prepared for
mining, which is include (1) data cleaning (to remove noise and inconsistent data)
and (2) data integration (where multiple data sources maybe combined), (3) data
selection and (4) data transformation. The next steps is (5) the data mining step (an
essential process where intelligent methods are applied to extract data patterns)
followed with (6) pattern evaluation and (7) knowledge presentation (where
visualization and knowledge representation techniques are used to present mined
knowledge to users) (Han et al. 2003).
Data usualy structured as an n×d data matrix, with n rows that correspond to
entities in the data set, and columns represent attributes or properties of interest.
Data mining process using quantitative technique which is comprises algorithms
that could use to discovering insights and knowledge from massive data. Several
disciplines that influence the development of data mining methods are statistics,
machine learning, pattern recognition, data base and data warehouse systems,
information retrieval, visualization, algorithms, high performance computing, and
many application domains (Zaki and Meira 2013).
Relief (relieable eliminated of feature)
Generally, a data set is a contents of attribute. Feature selection is the
problem of choosing a small subset of features that ideally is necessary and
sufficient to describe the largest concept. Feature selection is important to speed
up learning and to improve concept quality. Relief Method is a reliable and
practically efficient method to eliminate irrelevant features. Relief algorithm
composed by training data S, sample size m, and a threshold of relevancy τ,
Relief detects those features which are statistically relevant to the target concept.
τ encodes a relevance threshold (0≤ τ≤1) (Kira and Rendell 1992). The key idea
13
of Relief is to iteratively estimate feature weights according to their ability to
discriminate between neighboring patterns (Sun 2007).
Cosine Similarity
As part of the operationalization of several data mining algorithm, we need
to compare data quantitatively to determine similarity and proximity of data
characteristics. The distance measures data could use for computing the
dissimilarity or similarity of objects described by numeric attributes. Thus, the
purpose of data mining methods can be obtained such as clustering and
classification data from thousands of data attributes. Cosine similarity is a measure
of similarity that could use to compare documents or, say, give a ranking of
documents with respect to a given vector of query words. The computation based
on euclidean distance, which is conceptually it is the length of the vector (Kira and
Rendell 1992).
Fuzzy Associative Memory
Working with uncertain data is the reason why FAMs have been used in
many fields such as pattern recognition, control, estimation, inference, and
prediction. FAM was use to measure of how much one fuzzy set is a subset of
another fuzzy set, whose input patterns, output patterns, and/or connection weights
are fuzzy-valued. The simplest FAM encodes the FAM rule or association (Ai, Bi),
which associates, the p-dimensional fuzzy set Bi with the n-dimensional fuzzy set
Ai. More general, a FAM system encodes a bank of compound FAM rules that
associate multiple output or consequent fuzzy sets B1,..., Bs with multiple input or
antecedent fuzzy sets A1,...,Ar. We can treat compound FAM rules as compound
linguistic conditionals. Neural and fuzzy systems estimate sampled functions and
behave as associative memories, the computation process based on associative of
example data. That means FAM learning the association from samples (Kosko
1990).
FAMs belong to the class of fuzzy neural networks, which combine fuzzy
concepts and fuzzy inference rules with the architecture and learning of neural
networks (Bui et al. 2015). For traceability case, FAM method is used to predict
total handling time to cover several issue after customer complaint. FAM was used
because, it could be generate more objective rule that acquire from data sample or
data training. Thus, the knowledge and rule or relation between Fuzzy set of input
and Fuzzy set output could be naturally, and in many cases easily obtained. FAM
combine antecedent and consequent sets with logical conjunction, disjunction or
negation that would interpret the association linguistically as the condition (Kosko
1990). The associative memory means that this method allow for storage of
association of data and information and also the retrieval of the desired output data.
Response time is a key driver of customer satisfaction in case there is
complaint from customers, with ‘First Response’ time is particularly important.
Even when an issue cannot be resolved immediately, it is important that the
company heard the complaint and is working on a solution. One of action that could
use as first response is give an information about total handling time. Handling time
is amount of time for processing an issue. Meanwhile, total handling time means
total time spent for processing all issues over a given time period. Handling time
covers all activity, including elements such as reading tagging, marking sentiment,
14
looking up customer account info, making notes and drafting responses (Wilson
2014). We would try to construct association of handling time in traceability system
as development steps using Fuzzy Associative Memory.
PT Nusa Ayu Karamba
This research was conducted in a company called PT Nusa Ayu Karamba.
The company was established in May 2001. Nusa Karamba Aquaculture Farm &
Hatchery was established to begin breeding Grouper Fish in Pulau Seribu. Their
Aquaculture is located in Gosong Pulau Pramuka between Pulau Pramuka, Pulau
Panggang and Pulau Karya, Indonesia. They breed milkfish, Cod, Pompano,
Golden Trevally, Tiger Grouper, Barramundi and also produce boneless milkfish.
The whole farm has grown to cover an area of 3,000 sq. metres of various
facility buildings and floating cages. Although our current concentration is in
producing Grouper seeds ready for the market, we also have fingerlings and
Groupers of various sizes. Their original Broodstock came from the wild of
Makassar, Surabaya, Bali and Pulau Seribu.
The company have successfully bred the Panther and Tiger Groupers. They
have accumulated 50 Panther and 40 Tiger Grouper Broodstock at this moment.
Besides the Grouper Aquaculture venture, they would like to build further facilities
that would support Marine Tourism especially Educational Marine Tours and
Research purposes for Scientists and Students in the field of Fisheries and Biology.
(Anonim 2015).
3 METHODOLOGY
Framework
Mainly, traceability system consisted of two large components of system.
First, the system of data documenting at every traceable unit and product
information tracing from customers. System designing followed the mechanism of
system development life cycle (SDLC) (Kendall and Kendall 2011), which included
requirement analysis, design and development, testing and evaluation. Business
process model in documentation system was developed as the step of requirement
analysis to identified stakeholders’ roles and the rule that traced the system to reach
the aim of product data documenting. This interaction developed the first layer of
digital business ecosystem.
Data documenting application developed the second layer where system
designing and developing were done. Product data documenting included designing
database input application and query traceability application as digital species that
represented the roles of stakeholders. Query subsystem consisted of some
mathematic models which represented the activity of data query in real, included
model sorting and searching. System development was done by adding the ability
of data attribute similarity measurement using the method of Cosine Similarity to
documenting it. This system was also completed with the ability of predicting total
handling time using Fuzzy associative memory (Kosko 1990). Then, process of
system development was finished by testing and evaluation through verification and
validation. The flow of framework can be seen on Figure 6.
15
Figure 6 The framework of traceability system for frozen
shrimp product
Business Process Analysis
Analysis of the business process mechanism was done by using the BPMN
(business process model and notation). Development of BPMN aimed to identify
16
the business process stages in detail and completely in the digital traceability system.
A business process model is generally developed as the basis of discussion among
stakeholders that can support the process of communication and understanding by
visualizing the whole operational activity and some improvement steps (White and
Miers 2008). The output of this analysis are stakeholders identification, roles of
each stakeholders, rules in business process, data flow, and more which can support
a business activity. BPMN in this research was developed using PowerDesigner®
version 16.1. Notations in this BPMN were developed in “swimlane” which used
to help in making a separator in managing the activity of BPMN diagram, by
combining some notations. The main elements of a BPMN process are the "flow
objects", such as (1) activities, (2) events, (3) gateways and (4) sequence flow
(Derreck and Miers 2013).
Identification of System Component
System component was identified by observation, in-depth interview, and
questionnaire. The first method was done by observing the production process from
upstream to downstream. The existing business process be obtained by observe and
interviewing the management and technical staff of the companies. Observations
focused on identification of business process and system components that could use
to compose traceability system which includes the roles and interactions between
units, resource, rules, and data flow of information and documents that are currently
used in units of raw material handling and fish production units. In-depth interview
aimed to collect the more detail information about the business process in producing
frozen Vanname shrimps and technical matters by stakeholders. Distribution of
questionnaire was done in collecting data attribute and range data value from some
parameters of Vanname shrimps. After all information and data were collected
completely, the next step was system requirement analysis using the requirement
matrix.
Determine Critical Attribute
The determination of input data from stakeholders was done by using
questionnaire. Data was taken by using form TU1 in Appendix 1. Factor analysis
was done to identify the most important data in traceability. It was the basis for
source code application setting, that the documenting couldn’t be done further
before the most important data had been documented. The analyzing was done by
Relief algorithm (reliable eliminated of feature) with measuring the value of weight
attribute (wi). Relief algorithm chose the attributes based on average weight of each
attribute according to the threshold τ . Relief used p-dimensional Euclid Distance.
In this algorithm, first, defines ith feature as fi where F = {f1, f2,..., fk} where
fk € F, the weight of the ith feature as wfi where W = {wf1, wf2,…,wfk} where wfk €
W, an instance value of fi defines as xi where X = {x1, x2,…,xk} where xk € X. The
iteration runs using pairwise between data instance xi and the neighbor yj. Then in
each iteration (t = 1, 2,…, T), if the neighbor is the same class termed the nearest
hit (hi) where NH = {h1, h2,…, hk} where hk € NH and the other, if the neighbor is
different class termed the nearest miss (mi) where NM = {m1, m2,…, mk} where mk
€ NM. For each iteration, the feature weight wfi is then updated as follows
(Konokenko et al. 1996):
17
( n −1)
n
∑ ∑ diff ( x , y )
wi =
t
t =1 j = t +1
j
(1)
(n − 1)
The differences of feature values between two instances xi and yi (hi or mi) are
defined by following function diff (Kira and Rendell 1992):
  | xt − y j | 
+ 

  maxi − mini 
diff ( xt , y j ) = 
  | xt − y j | 
 −  max − min 
i
i 
 
Where:
wi
xt
yj
maxi
mini
(if xt and y j are different class)
(2)
(if xt and y j are the same)
= Weight of attributes-i
= instance of attribute-t (t= 1,2,3…n)
= neighbor of attribute-j (j=2,3,4…n)
= maximum value of attribute-i
= minimum value of attribute-i
Design Traceability Information System
System design process focused to providing component for acquiring,
processing, organizing and maintaining information generated on each stakeholder.
Several programming techniques required for development capturing and acquiring
system of data and information, for instance JAVA, XML and web site
development-based programming (Gunasekaran dan Ngai 2004). Therefore in this
research we software developed by JAVA programming language. NetBeans 7.3.1
used as integrated development environment (IDE) to implement the source codes.
Design information database system can adopt Sybase, Oracle, FoxPro, Access,
SQL server and other large database systems (Lei dan Wen-li 2012). In this research,
SQL Server is used. Traceability system is divided into two major parts, they are
capturing data and tracing data.
Capturing Data
The entire data attribute was documented during the activity of handling
process in the data application form on each stakeholder. Every stakeholder carried
out the same process namely data input process into desktop computers to be stored
in a server. Software application for capture data designed by adding several service
to inputting relevant shrimp data in the database system. Both batch quality and
batch activity data corresponding to a shrimp batch must be inputted. Digital
traceability system that developed in the European Union relied on barcodes to
identify for traceable units and XML for internet-based transfer of data between
operators (McMeekin et al. 2006). Therefore, the software would develop by
including capability to generate a barcode label and then printed it. The printed
barcode in label became the key for product registration handled by using hand held
barcode scanner to icquiring information on previous traceable unit.
18
Tracing Data
A database for this traceability information system was constructed for data
query using several computation model follow as tracing process manually, such as
sorting and searching, then arrange as source code software by using java
programming. A relational database management system is used to perform tracing
processes.
System Computation
Generally, the traceability system is focused on component modeling for the
process of collecting information and information retrieval. However, some
computation functions are needed for some goals. In order to ensure that processing
practices comply with the food safety regulations, the supply chain actor must be
able to show that the processing conditions used to manufacture a product
(temperature, environmental condition etc) are in compliance with the food safety
regulations (Thakur and Hurburgh 2009). Thus, in this research, the function of
system computation was used to determine the level of data similarity from the
documentation, which was searched by defined standard data in database.
Data similarity measurement was done by using the data mining technique
with the method of Cosine Similarity, which was measured by the formulation
below (Han et al. 2006):
sim ( X1 , X 2 ) = X1t .X 2 || X1 |||| X 2 ||
(3)
Where X1 and X2 is two vectors for comparison. X1 is used as variable for
data which is collected from field measurement, meanwhile X2 is variable for
standard data value that defined by the company. Notation ||X|| is Euclidean norm
of vector X = (x1, x2, x3….xp) which defined as:
(
|| X || = x12 + x22 + x32 + ... + xp2
)
1
2
(4)
(3)
Where:
x1, x2,…xp = Element of vector X
p
= Number of elements.
In the other hand, as the initial respond to finish the correcting process on
searched products, the measurement of total handling time was used to provide
initial information about time needed in solving the actual problem when there was
any complaint from the customers. In this research, handling time was defined as
time needed to do some activities after the process of data tracing from complained
products by customers. Determination of total handling time was formulated using
the Fuzzy Associative Memory (FAM) which mapped and predicted the total
handling time if there was any variable change. Before defining the handling time
computation capability in the source code, FAM was formulated using variables
that affect the handling time. The set of variables is partitioned into three linguistic
scale (Choudhury et al. 2002) using triangular fuzzy set membership function as
shown on Figure 7.
19
X
1
µA [ X ]
0
a
b
[X] A
c
Figure 7 Triangular membership function of Fuzzy set X
for variable A
The membership value computed as follow:
0
; x ≤ a or x ≥ c


µ A [ X ] =  ( x − a ) / (b - a ) ;
a≤ x≤b
 ( c − x ) / (b - c ) ;
b≤ x≤c

(5)
(4)
Where:
µ A [ X ] = Membership function of an item value x on Fuzzy set X at variable A
a, b, c = a, b, c are parameters of membership function curve and b is midpoint.
The value of the membership function formed into elements of the matrix
FAM, whereby the input variable set as matrix A and output variable set as matrix
B. the association between matrix A and B defined on FAM rules. Matrix A and B
encoded using Correlation minimum encoding scheme, then resulting matrix
memory (M) as the correlation matrix FAM (Kosko 1990), which compute as
follows:
(6)
with m = min(a , b )
M = AT o B
ij
i
j
The computation for handling time then defined on source code to obtain the
output value B using the relation composition of A and M. The output value
encoded using max-min composition relation which computed as follows:
B = AoM
Where:
M = Matrix memory
A = Vector input Fuzzy set A
B = Vector output Fuzzy set B
with b j = max min( ai , mij )
1≤i ≤ n
(7)
mij = As component M at row-i and coloum-j
ai = As component A at row-i
bj = As component B at coloum-j
The result of matrix operation could computed using the Fuzzy centroid
defuzzification scheme, which compute using this equation:
p
∑ y j ⋅ µB [ y j ]
B* =
j =1
p
∑ µB [ y j ]
j =1
(8)
20
Where:
B*
µB[ y j ]
yj
p
= Single output Fuzzy
= Membership value of Fuzzy set yj for output (B) variable
= Value in the output universe
= Number of output element
Verification and validation
The agreement of requirements and system verification documented as a
requirements verification matrix (RVM) (Wasson 2006). Inspection method was
chosen to proven whether the logic of the model in each stakeholders is
implemented correctly or not. The system is checked during design and develop
phase and when finished. Verification stages perform in also to check the
completeness and correctness of logical function implementation through several
actions as follows:
1. Testing, inspection, analysis of system mission and analysis system
specification (Meyer et al 2007), by using white box testing.
2. Based on RVM we define statement and question to perform general checklist.
3. To verification that data and information could collect and retrieve, several
devices has provided for test method, such as barcode scanner and barcode
printer, to prove automatically input and generating report.
Validation has performed using user interview method and simulation test
to ensure that system design comply requirements and satisfies the user needs. The
validation has to confirm that the simulation system or model corresponds to the
reality. This means that the investigated aspect has to be simulated realistically, and
the results have to be comparable to the real user espectation. Validation stages,
perform by conduct several steps (Meyer et al. 2007), based on black box testing as
follows:
1.
2.
3.
4.
Checking simulation and investigate system behavior.
Documenting output of the investigation in order to testing, recognize and
eliminate error in several part, such as:
- In the software program it self ;
- In the program run ;
- In the user interface, and
- In the original real world system
Evaluate and comparing to the real-world system are taken concerning the goal
of the project.
Provide recommendations for the investigated system based on the simulation
results.
21
4 RESULTS AND DISCUSSIONS
Identification of Existing Business Process
Field Observation
Field observations performed several times at the company during which all
stages of production were carried out from upstream to downstream. Based on
observation and interview process, the basic business processes for handling
fisheries product are generally divided into 4 diferent location such as breeding unit,
ongrowing farm units, processing units and sales unit as shown in Figure 8. The
activities that take place at breeding unit are divided into three subprocesses. The
process begins by choosing broodstocks to produce seeds, followed by hatching
then transferred into the seed ponds for rearing the seeds until larvae are ready to
be harvested and could brought up in ongrowing ponds. The breeding units can be
seen in Figure 9.
The larvae transferred to ongrowing unit that can be seen on Figure 10.
Larvae reared to the standard of weight and size to be harvested. On the harvesting
time, fish harvested and stored at the countainer filled with sea water and ice, then
transferred to the processing unit. There are three subprocesses in this unit, fish is
received then processed by cleaning, sorting by size, freeze and packing in plastic
packaging using vacuum sealer and the last labeling the packaging. After that, the
product transferred to the sales unit then perform stock control to meet the purchase
orders at their store unit or distributed to retailer.
Figure 8 Current business process and provision of
information at the company (Adopted from
Parenno-Marchante et al. 2014)
Data Identification
The company perform manual collection of information about product
handling, such as the number of seeds as well as the type and amount of feeds by
22
filling the information board which is also containing standard operation procedure
and hygiene procedure. They also measuring processing condition such as pH,
temperature and water salinity. The staff just entered the data into paper logs that
stored by the administrator of the company. Although the company has perform
data and information collection, it is not intended to support the product tracing
process because the datas just used as a report of the production process and have
not been integrated. The company has marked the ponds code that used during the
production process, but not yet integrated with the product handling data. The range
value of processing condition parameters obtained form questionnaire. Appendix 1
show the result of received answer from technical staff of the company after fill out
the Questionnaire TU1.
Detailed information about the parameters measured in the process of
handling products, Standard Operation Procedure, and identification number
principle is done through questionnaires. Based on the answers given by the
operational staff, we have identified several attributes and tolerable value as a limit
value for the successful cultivation which could affect the quality of the fish.
Recapitulation process of the measured parameters and ranges of values shown in
Table 2, meanwhile identification result about the coding rule is shown in Table 3.
Figure 9 Breeding unit
Figure 10 Ongrowing unit
23
Table 2 Data identification and range of value processing parameters
No
Supply Chain unit
1 Breeding
seed production
seed harvesting
2 Ongrowing
rearing
harvesting
3 Processing
4 Cold storage
5 Retailer
Temperature
(°C)
29-33
19-22
29-32
19-22
15-18
NA
NA
Data attribute
Salinity
pH
(ppt)
7.5-8.5
7.5-8.5
7.5-8.5
7.5-8.5
7.5-8.5
7.5-8.5
NA
NA
DO
(ppm)
32-33
28-31
5-7
9-12
25-33
29-33
28-31
NA
NA
4,7-7
9-12
9-12
NA
NA
DO = dissolve oxygen; NA= not available
Table 3 Coding required and existing coding system
No Supply Chain
unit
1 Breeding
2 ongrowing
3 Processing
4 Cold storage
5 Retailer
Code required
Seed ID
Seed Pond ID
Feed Supplier ID
Rearing Pond ID
Feed Supplier ID
Batch ID
Packaging ID
Cold storage ID
Retailer ID
Coding
system
No
Yes
Yes
Yes
Yes
No
Yes
No
No
Coding format
NA
1,2,3 etc
N, PL
Letter with figure
Based on size
NA
Type, size, date
NA
NA
NA= not available
Requirement Analysis
Business Process Analysis
The existing business process of the company, found from field observation.
Based on result of current business process analysis we create a new business
process model that would made digital traceability system worked properly.
Business process in traceability system is modeled in BPMN 2.0. The Development
of BPMN is conducted. It is started from the making of simple flow chart, granting
information related roles, process, data and information to description, therefore it
can be analyzed and simulated. System analysis is conducted for parse a system be
resolved into components so it the interaction between components and its
environment can be seen. Results of analysis showed the capacity of the system as
seen from its ability to add value from input to output. System has divided into four
structure systems:
1. Input of traceability system
This system requires data related to product, processes and product quality
as main input. Data related to product include its product identity code along
24
with various identity components that support the formation of these products.
Meanwhile, the data related to the process cover some of the indicators of the
process that are set up on the stage of the seed production. Among them are pH
of water, water salinity, survival rate, and temperature of water. The data related
to the quality of the standard value according to SNI are the total plate count
(TPC), the levels of lead, the levels of histamine and others. This system,
however, is not documenting related data quality due to lack of infrastructure
system.
2. Pre-process on collecting data
The results of identification of the data attribute are then observed and
documented in the application form for a period of time during material handling
process.
3. Collecting data process
The main processes include documentation of process traceability system
using an application data input of each stakeholder and tracing product process
from end user stakeholder. Every stakeholder performs the process of
documentation into application data input that was installed in the desktop
computer on each unit of stakeholder.
As an example, in the breeding unit, the data attribute among others were
seeds ID, pond ID, provided feed mill supplier, pond water temperature, pH,
water salinity etc (Table 4). After login into software application, users should
inputting seed ID data, choose a pond ID, feed supplier ID and all of measured
data during handling process. Another stakeholder would perform the same
stages after handling process finished. The process is then continued by printing
the report in the form of label contains barcode ID and and destination unit as
product identification for the next handling process. The barcodes on the labels
function as product identification that can be read using barcode scanner.
Readable barcode labels subsequently can be added to the data on the next
process. The barcode is printed back and imprinted on the next product label. A
fragment on business process for barcode generate shown on Figure 11. For
tracing purposes, it is essential to know the relationship between ingoing and
outgoing idents of a relation type, in example knowing which shrimp batch or
pond ID is inside a box of shrimp product (Hulzebos and Koenderink 2006).
Business process for another stakeholder can be seen on Appendix 12-15.
Table 4 Seed data documentation result on breeding farm
Seed ID
Number of
seed
Pond ID
Feed
supplier
ID
Date of input
data
Pond
tempera
ture
(°C)
pH
Sali- Date of hatch
nity
(ppt)
B191020143
B211020144
B251020145
1540000
1800000
2000000
S00001
S00003
S00002
S00003
S00003
S00003
25/10/2014
28/10/2014
31/10/2014
29
29
29
7.8
7.8
7.8
32
32
32
26/10/2014
27/10/2014
30/10/2014
Seed ID: seed identification number; Pond ID: body of water identification number, Feed supplier ID: vendor that supply
feed for cultivation process identification number.
4. Output of traceability system
The data set will store in the traceability repository include the relevant
traceability data generated during the company operations (Parreno-Marchante
et al. 2014). Data will distributed to the query application by getting input
product code from customer. The retailer will get traceability reports that
25
following typical several information for instance (1) component source and
quality attribute, (2) recall list which contain all the needed information to
contact affected customers and (3) suspect lots or unit process with nonstandard
procedure.
Based on the analysis, traceability system must fulfill basic architecture of
Input-Process-Output components as shown in Figure 12. Traceability based on
DBE could be seen on Figure 13. Each actor in the traceability system is a model
of organism in digital business ecosystem (DBE) which interact each other in data
capture and tracing process (1st layer). Through the development of DBE, every
technical phase in the process of documenting data can be conducted by using the
support of ICT based infrastructure that is analogized as digital species in digital
ecosystem. The technique of product search can also be represented in the form of
formulation and logic of computer programming hence the digital application
design is attained for the process of data documenting and searching (2nd layer).
Breeding unit
Figure 11 Fragment of seeds data documentation process
26
System Bound and Environment
Input:
Product Data
Process Data
Stakeholder:
- Breeding unit
- Processing unit
- etc
Role:
- Capture Data
- Tracing
process
Traceability System
Output:
Tracing Report
Recall Report
Regulation Digital
Device
Figure 12 Component for traceability system (Wasson 2006)
Figure 13 Structural coupling between supply chain
ecosystem and digital ecosystem in traceability
system (Nachira et al. 2007)
Result of Determine Critical Attribute
Information about quality is one of information categories that needs to be
captured by each supply chain actor (Takur and Donnelly 2010). We assumed that
there were some data attribute represent quality of handling process, such as
average pond water temperature, pH of water, water salinity and DO (dissolved
oxygen). Based on questionnaire at Appendix 1 only water temperature, pH and
water salinity that always measured until processing unit and they were numerical
type showing a value, then we defined as attributes which were analyzed by using
Relief algorithm. That attributes used as quality process parameter in each
production stages, thus that attributes must documented for complete information.
Table 5 shows result of Relief method utilization to determine critical
attribute. The results of attribute analysis using Eq. (1) and (2), showed attribute
sequence that influence the system and become the consideration in determining
critical data attribute that were needed to be constantly documented. Based on
Relief computation, average water temperature was chosen as the critical attribute.
Thus on source code application setting, we define that water temperature as “not
null” attribute on database structure. The documentation process couldn't be done
further before temperature has been documented.
27
Table 5 Results of attribute analysis with Relief method
Data attribute
Average pond water temperature
pH of water
Water salinity
Average weighted
0.694
0.132
0.333
Rank
1
3
2
Relief: reliable eliminated of feature
Design Traceability Information System
Based on data attribute determination, we create a database for traceability
systems. The database established using the MySQL database system, which could
be made using XAMPP 1.7.7. Any relevant data needs to be registered and
regulated in the database. The main system is divided into five subsystems that
represents the supply chain actor in frozen shrimp production. They are breeding
unit, ongrowing unit, processing unit, cold storage unit and retailer. The software
application was designed to be used on each unit. Several same unit with the same
software will form a community in the digital ecosystem which interact each other
for collecting and exchanging data between communities. A traceability system on
DBE, could be performed if it is composed of digital species (DS) with different
but complementary capabilities.
Capturing Data
The software application provide several field data to capture several
information related product information, process information and quality
information (Figure 14). The computer that installed on each unit with different
applications running on them, are all different kinds of digital species (DS). As an
example for breeding unit, the stage of seeds handling process ended when the seeds
grow turning into larvae that are ready to be transferred into rearing pond in the
Ongrowing unit. The operator would capture data start from rearing the seeds until
harvest the seeds.
Figure 14 Application interface of seed data input
For example, after the entire data was collected using application form, data
was inputed into input data software (DS1) that runing on computer desktop on
28
breeding unit. Seed ID, pond ID feed supplier ID and number of seeds was
information relating to the products that represent the product batch identification,
location and product from the outside that included in the process of handling.
Standard operation procedures (SOP) data represents the process information
whereby the user could perform the checklist if the SOP was done, meanwhile
temperature, pH and water salinity was represents information about quality of
cultivation processes.
Data input application in desktop computer would save data into database
after clicking “input” button then it would be automatically generated the barcode
label, by clicking “print label" menu. The barcode label contains seed ID barcode
and also pond ID for the process of enlargement shrimp larvae. By scanning the
barcode label using barcode scanner (DS2), seed data would retrieved and shown
on input data software that running on desktop computer at ongrowing unit (DS3).
Operator at ongrowing unit would be able to read only the information about the
origin of the seeds batch that will be transferred into enlargement pond. Interaction
between digital species were illustrated in Figure 15.
The dataset from Ongrowing unit show in Table 6. Pond ID was intercorrelated with data attribute from ongrowing process, such as feed supplier ID and
date of harvest and other quality process (pH, temperature, salinity, DO). During
the harvesting days, the data of yield and transportation are collected by an
application form to get data about harvest date and harvest container ID which used
to.
Figure 15 Use of digital device in traceability system
29
It is important to collect data accurately and pass on the information to the
next actor in the supply chain. Figure 16 shows the shrimp supply chain and the
information that should be recorded and passed on to the next link in the supply
chain by each actor. It also shows that which information about a shrimp batch
should be passed on to the next actor in the chain. The superscripts link the
information that is passed on between supply chain actors. When all the relevant
information is recorded and passed onto the next actor, the shrimp batch and their
properties used in the final product can be traced back to the origin. Also, the shrimp
batch from the farm can be tracked forward to the retailer. It can be seen from Fig.7
that not all of the information is passed to the next link in the supply chain. However,
it is important that all the relevant lot information is passed to the next link. This
information should be sufficient to support the tracing performance.
Table 6 Shrimp data documentation result from ongrowing unit
Seed ID
Number of
seed
Pond ID
Date of input
data
Pond
temperature (°C)
B191020143
B191020144
B211020145
1500
1670
1230
G00001
G00001
G00001
19/12/2014
19/12/2014
19/12/2014
29
31
31
pH
Salinity
(ppt)
8
28
7.2 32
7.9 30
DO
(ppm)
Date of harvest
6
5.5
6,5
24/12/2015
25/12/2014
26/12/2014
Table 7 Shrimp data documentation result from processing unit
Pond ID
Line ID
Seed ID
Number
of seed
Container
ID
Date of input
data
Date of processing
G00001
G00003
G00004
L00001
L00002
L00002
B191020143
B191020144
B211020145
1500
1670
1230
C00002
C00006
C00004
30/12/2014
30/12/2014
31/12/2014
30/12/2014
30/12/2014
31/12/2014
Line ID: line processing identification number, Container ID: boxes used for shipments identification number.
Figure 16 Possible information exchange between different actors in the frozen
shrimp supply chain (Adopted from Thakur and Hurburgh 2009)
30
Tracing Data
In case for handling customer complaint, the traceability system will
perform tracing process. Consumers can select two sorts of input methods for
entering the traceability code at the retailer unit, they were barcode scan and
keyboard input. If it has been selected the barcode scan method, the scanner
associated with the terminal is prepared to read the barcode label. Software
application running on computer desktop can retrieve data about the product using
tracing software by read barcode on product packaging using barcode scanner.
The alternative method was that the user could input the traceability code
from a keyboard. After the traceability code has been entered and the ‘trace’ button
clicked, the information would popup displaying the traceability information. The
tracing application will generate tracing report, some information included in the
tracing report such as description of the product, expired date of product, flow chart
of the process, include identification number of unit process or material and
temperature process and water pond salinity, similarity value of the process with
standard procedure and then duration of tracing.
Consumer confidence could affect consumer preference about the products
that have an impact on product positioning in the market. One of the reason that
affect consumer confidence is that products comply the required criteria on quality
control parameters. Therefore the other output of traceability system is similarity
value that could be a representation of the company production quality to process
the product which is traced. The similarity measure is an essential concept in
information retrieval. This computation usulally used to measure the relevance of
documents in information retrieval. In traceability system. The similarity value
could used to measure the company performance on product handling and
cultivation process. The document replaced by value of data attribute that represents
the characteristics of an object.
Table 8 Standard data attribute of quality reference
pH of Water salinity DO (ppm)
Attribute
Symbol Average
temp (°C) water
(ppt)
Standard
Measurement data
X1
X2
31
23
7.5
8
33
32
6
5.5
The tracing software perform similarity measurement by compare of
numeric value measurement data with standard data attribute which is determined
from range value of attribute that user defined at questionnaire at Appendix 1.
Suppose that X1 and X2 are the first two vectors in Table 8. That is, X1= (31, 7.5,
33, 6) and X2 = (23, 8, 32, 5.5). The similarity between the two vectors could
compute using Eq. (3) and (4). From computation experiment of cosine similarity
between the two vectors, we get similarity value was 0,991. Based on the results of
similarity measurement, it indicated that the quality parameter on seeds handling
and production process from the searched seeds ID was almost similar to the
standard parameter with value of 0,991. The similarity value that almost close to 1
means that data that has measured is similar with the standard that we defined. Thus,
it could claim that the seeds ID were in the standard of cultivation process.
31
The other computation result was about prediction of total handling time to
resolve several issues that arise after the defect product traced. For instance,
replacement of the product, recall product by information about recall list contact
number of affected customers and inspection process on each production unit about
food safety standard. Replacement the suspect products with new products were
affected by inventory finish good. Meanwhile, recalling process of suspect products
from market affected by the amount of recall products (recall product). For
inspection process each unit in supply chain system affected by the amount and the
time of unit checking activity consisted of checking the implementation of standard
operation procedure (SOP), quality parameter analysis of production tools
(inspection) and checking sample of fish based on SNI standard as shown on
Appendix 5. The formulation of FAM can be seen on Appendix 3-7.
From FAM formulation that consists of 27 rules (Appendix 8), if there are
case of incidents of contamination fish products whereby the inventory conditions
as much as 4 tons, product recall amount 21 tons and inspection require 25 hours,
result of computational experiment (Appendix 9) using Eq. (6), (7) and (8), showed
that total handling time for this case takes 66 hour. The results of prediction total
handling time used as advise for customers not to buy the product during handling
the contamination issues. This information could used to convince the authorities
about the company willingness to resolve the problem within the period of time that
have predicted
System Evaluation
In order to achieve the last objective, this system has verify based on
requirement verification matrix (RVM). Systen verification perfom by checking the
source code and test the performance using supporting device to prove that system
component works properly. Detail of RVM can be seen on Appendix 5, and after
we define the verification which is require on system mission, we try to perform
general inspection and checklist based on RVM.
For example, the verification step was perform on breeding unit interface as
part of this system. The first step, we try to inputting user name and password on
login interface, if we click the login button, there will a notification that login was
success. The data was collected using application form as shown on Appendix 6.
After we perform a login steps, we try to inputting data into software application.
There will a notification that our data was stored into database and then generate a
barcode label. That means the logical function for documentation process has
verified, as shown on Figure 17a and 17b.
(a)
32
(b)
Figure 17 (a) Inputting data process; (b) Database
interface on traceability system
Based on verification stages, this system is verified and could perform input
data process, generate barcode label using barcode printer and read the barcode
label using barcode scanner. From validation stages, we have validate that this
system was satisfied and comply with specification requirement. Another example
of verification process was perform on ongrowing unit to acquire data from
database using seed ID as a key code. After we scan the barcode or choosing
manually the seed ID that has stored (Figure 18a and 18b), the data has retrieve into
input data interface along with other data attributes as shown on Figure 19.
(a)
(b)
Figure 18 (a) Scan the barcode using barcode scanner;
(b) Choosing the seed ID manually
33
Figure 19 Result of retrieval data process
Traceability management information system provided intangible advantages
for instance practicality, security and the deliverability of data for each stakeholder.
This system could save, organize and emerge data easier. Besides financial benefits,
this system give improvement in the quality of information for management
decision-making, prevents in error documentation processes, perform check
similarity between field data and standard data and able to compute handling time.
Each data saved with high security from lost and easy exchanged to the other
stakeholder by the local network among the computer. Thus, the propose system
could solve the biggest challenges in order to exchange information and develop a
new food electronic chain traceability system. However, this system has several
disadvantages, for instance requires high integrity of the operator, thus we could
not ensure that data is accordance to the real condition. Besides, this system also
requires high cost for investment, because this system could be applied only to the
companies that have adopted ICT in their business activities and the stakeholders
have to install the software application to join the digital ecosystem for traceability
system.
5 CONCLUSIONS AND RECOMENDATIONS
Conclusions
Business process analysis show that, there were five stakeholders taking role
in traceability system for frozen Shrimp product such as breeding unit, ongrowing
unit, processing unit, cold storage unit and retailer. We have identified that each
stakeholder have a role for collecing data related the product handling process,
meanwhile tracing process would perform on retailer. There are five system mission
that have to perform among stakeholders.
We have been design a traceability software which is composed by input data
and tracing application. We have succeded using java programming language as a
foundation to implement the sounce code. The traceability could perform all system
mission based on stakeholder roles such as data capturing, data store, product
labelling, data retrieval and supported by computation capability such as similarity
measurement and prediction handling time.
34
The results of verification model show that the model used was able to
produce the expected parameter according to its purpose. Similarity measurement
could claim that product is safe to consume. Handling service time prediction using
FAM could predict total time spend for processing a defect product issue. Thus, this
system could be verified and valid.
Recomendations
Based on the results of this research, we recommend to the further research
to use sensor and RFID device for collecting data automatically. Thus the data
collected could more precise, accurate and customers would trust with their validity.
We also suggest this system as one of the requirements of the fishing industry
practices in Indonesia and outlined in the Memorandum of Understanding. The
government needs to introduce ICT adoption process for business purposes, thus
the groups of fishermen which is one of the fishery supply chain actors could be
participated and join into the digital ecosystem and also could support connectivity
of SME in fisheries commodity in order to data collection and exchange data. To
prevent high cost investment, the system deployment needs collaboration between
different stakeholders.
REFERENCES
Anonim. 2015. Nusa Karamba Aquaculture Farm and Hatchery. [Internet].
[downloaded 2015 April 20th]. Avaiable at: www.nusafish.com
Bui TD, Nong TH, Dang TK. 2015. Improving learning rule for Fuzzy Associative
Memory with combination of content and association. Neurocomputing.149:59–
64.doi: 10.1016/j.neucom.2014.01.063.
Ben-hai X, Rung-ting F, Zhao-hui L, Qing-yao L, Liang Y, Jia-rong P. 2010. A
Solution on pork quality traceability from farm to dinner table in Tianjin City,
China. Agric Scien Ch. 9(1):147–156.doi:10.1016/S1671-2927 (09)60078-X.
Bosona T, Grebesenbet G. 2013. Food traceability as an integral part of logistics
management in food and agricultural supply chain. Food Contr 33:32-48.
doi:10.1016/j.foodcont.2013.02.004.
Choudhury JP, Sarkar B, Mukherjee SK. Forecasting of engineering manpower
through Fuzzy Associative Memory neural network with ARIMA: a
comparative study. Neurocomputing. 47:241-257.
[EAN-UCC] European Article Numbering-Uniform Code Council .2003. EAN
UCC Traceability Implementation:Trace-I Project Deliverable. [Internet].
[downloaded
2015
March
26th].p
3-106
Avaiable
at:
http://www.gs1.org/docs/TRA_0404.pdf.
[FAO] Food and Agriculture Organization. 2014.The State of World Fisheries and
Aquaculture 2014. Rome (IT): FAO
Ferronato P. 2007. Ecosystem oriented architecture (EOA) vs SOA. In: Nachira F,
Nicolai A, Dini P, Louarn ML, Leon LR, editor. Digital Business Ecosystems.
Luxembourg: European Commission. p 111-118.
Gunasekaran A, Ngai EWT.2004. Information systems in supply chain integration
and management. E J O Research. 159:269 -295. doi:10.1016/j.ejor.2003.08.016
35
Hadzic M, Dillon TS, Chang E. 2007. Use of digital ecosystem and ontology
technology for standardization of medical records. IEEE DEST: 595-601
Hadzic M, Dillon TS. 2008. Application of digital ecosystem in health domain.
IEEE DEST: 543-547
Han J, Kamber M, Pei J. 2006. Data Mining Concepts and Techniques. 3rd ed.
Waltham (US): Morgan Kaufmann Publisher.
Hu J, Zhang X, Moga LM. 2012. Modeling and implementation of the vegetable
supply
chain
traceability
system.
Food
Contr.
30:341353.doi:10.1016/j.foodcont.2012. 06.037
Hulzebos L and Koenderink N. 2006. Modelling food supply chains for tracking
and tracing. In: Furness A, Smith I, editor. Improving Traceability in Food
Processing. North America (US): Woodhead Publishing Limited. p 67-87
Kendall KE and Kendall JE. 2011. System Analysis and Design. New Jersey (CA):
Pearson Education, Inc.
Kira K, Rendell LA. c1992. The feature selection problem: traditional method and
a new algoritm. AAAI.92:129-134.
[KKP] Kementerian Kelautan dan Perikanan.2013.Marine and Fisheries in Figures
2013. Jakarta (ID): KKP
[KKP] Kementerian Kelautan dan Perikanan.2007. Keputusan Menteri Kelautan
dan Perikanan Republik Indonesia Nomor Kep.01/MEN/2007 tentang
Persyaratan Jaminan Mutu dan Keamanan Hasil Perikanan pada Proses Produksi,
Pengolahan dan Distribusi. Jakarta (ID): KKP
Kononenko I, Robnik-Sikonja M. 2003. Theoretical and empirical analysis of
ReliefF and RReliefF. Mach Learn J. 53:23-69
Kosko B. 1990. Neural Network and Fuzzy System, A Dynamical Systems Approach
to Machine Intelligence. New Jersey (CA) Prentice- Hall, Englewood Cliffs.
Lei L, Wen-li P. 2012. Research on design information management system for
leather goods. Phys Proced. 24:2151-2158.doi:10.1016/j.phpro.2012.02.316
Leon LR. 2007. A Cost-benefit analysis framework for assessing the net benefits of
digital business ecosystem implementation in Europe. In: Nachira F, Nicolai A,
Dini P, Louarn ML, Leon LR, editor. Digital Business Ecosystems.
Luxembourg: European Commission. p 83-91.
Meyer UB, Creux SE, Marin, AKW.2007. Process Oriented Analysis, Design and
Optimization of Industrial Production System. USA: Taylor & Francis Group,
LLC.
McMeekin TA, Baranyi J, Bowman J, Dalgaard P, Kirk M, Ross T, Schmid S,
Zwietering MH.2006. Information systems in food safety management. Food
Microbiol.112:181–194.doi:10.1016/j.ijfoodmicro.2006.04.048
Mgonja JT, Luning P, Van der Vorst JGAJ. 2012. Diagnostic model for assessing
traceability system performance in fish processing plants. J Food Engineer.
118:188–197. doi: 10.1016/j.jfoodeng.2013.04.009
Nachira F, Dini P, Nicolai A. 2007. A network of digital business ecosystem for
Europe: roots, processes, and perspectives. Di dalam: Nachira F, Nicolai A, Dini
P, Louarn ML, Leon LR, editor. Digital Business Ecosystems. Luxembourg:
European Commission. p 1-20.
Nidhra S, Dondeti J. 2012. Black Box and White Box Testing Techniques –A
Literature Review. IJESA. 2(2):29-50.
36
Olsen P and Borit M. 2013. How to define traceability. T F Scien & Technol.
29:142-150.doi:10.1016/j.tifs.2012.10.003
Parreno-Marchante A, Alfarez-Melcon A, Trebar M, Filippin P. 2014. Advance
traceability system in aquaculture supply chain. J Food Enginer. 122:99–
109.doi:10.1016/j.jfoodeng.2013.09.007
Pranata I, Skinner G. 2009. Digital ecosystem access control management. WSEAS
Trans Inf Sci Appl. 6:926–935.
Qian JP, Yang XT, Wu XM, Zhao L, Fan BL, Xing B. 2012. A traceability system
in corporating 2D barcode and RFID technology for wheat flour mills. Comput
and Electron Agricult. 89:76–8.doi: 10.1016/j.compag.2012.08.004
Scheer FP. 2006. Optimising supply chain using traceability system. In: Furness A,
Smith I, editor. Improving Traceability in Food Processing. North America (US).
Woodhead Publishing Limited. p 52-64
[BSN] Badan Standardisasi Nasional. 2006. SNI 01-4110.1-2006 Ikan
Beku−Bagian 1: Spesifikasi. Jakarta (ID): BSN
Sun Y. 2007. Iterative Relief for feature weighting: algorithms, theories, and
applications. IEEE Trans Pattern Anal Mach Intell. 23(6):1-17.
Thakur M, Donelly KAM. 2010. Modeling traceability information in soy bean
value chains. J Food Enginer. 99:98–105.doi:10.1016/j.jfoodeng.2010.02.004
Thakur M, Sørensen C, Bjørnson FO, Foras E, Hurburgh CR. 2011. Managing food
traceability information using EPCIS framework. J Food Enginer. 103:417–
433.doi:10.1016/j.jfoodeng.2010.11.012
Thakur M, Hurburgh CR. 2009. Framework for implementing traceability system
in the bulk grain supply chain. J Food Enginer. 95: 617–
626.doi:10.1016/j.jfoodeng.2009.06.028
White SA, Miers D. 2008. BPMN Modelling and Reference Guide. Florida (US):
Future Strategies, Inc.
Wasson CS. 2006. System Analysis Design and Development. New Jersey (CA):
John Wiley & sons, Inc.
Wilson MH. c2015. The Defnitive Guide to Social Customer Service. 3rd ed.
[Internet]. [downloaded 2015 January 7th].p 3-41 Avaiable at:
http://landing.conversocial.com/the-definitive-guide-third-editionthankyou?submissionGuid=b50940a1 68d3-40e2-b805-b9006a745f3b
37
APPENDIXES
Appendix 1 Questionnair TU1
KUESIONER
TU1
Yang terhormat Bapak dan Ibu
Untuk keperluan analisis dan desain sistem traceability produk perikanan,
kami mengharapkan peran serta bapak dan ibu dalam mengisi/melengkapi
pertanyaan di bawah ini baik berupa fakta, data dan informasi. Sampaikan secara
tepat dan benar, mana yang berupa pengisian tanda (O) dan mana yang memerlukan
kelengkapan keterangan, Terima kasih.
A. Breeding Unit
No
A.1
Pertanyaan
Jawaban
Memetakan atribut data
A.1.1 Parameter apa saja yang diukur a. Suhu
/dicatat
selama
proses b. Salinitas
pembenihan?
c. pH
d. DO
A.1.2 Berapa kisaran nilai minimum dan a. Suhu 29⁰C – 33⁰C
maksimum untuk setiap parameter? b. Salintas 32 ppt – 33 ppt
c. pH 7.5 ppm – 8.5 ppm
d. DO 5.0 ppm – 7 ppm
A.2
Sistem Pengkodean
A.2.1 Apakah ada pemberian kode pada a. Ada
benih?
b. Tidak ada
(Lingkari yang sesuai)
A.2.2 Bagaimana format kodenya?
A.2.3 Apakah ada pemberian kode pada a. Ada
kolam
b. Tidak ada
(Lingkari yang sesuai)
A.2.4 Bagaimana format kodenya?
Kode biasa pake angka
A.2.5 Apakah ada pemberian kode pada
pemasok pakan
(Lingkari yang sesuai)
A.2.6 Bagaimana format kodenya?
A.2.7 Siapa saja nama pemasok pakan?
a. Ada
b. Tidak ada
Sesuai size yang digunakan
a. Radiance
b. Pt Inve
c. Pt beinmeyer
38
A.3
Pencatatan Panen
A.3.1Data/parameter apa saja yang a. Suhu
dicatat/diukur saat panen?
b. Salinitas
c. pH
d. DO
A.3.2 Berapa kisaran nilai minimum dan a. Suhu min 19⁰C max 22⁰C
maksimum untuk setiap parameter? b. Salinitas min 28 ppt max 31 ppt
c. pH min 7.5 ppm max 8.5 ppm
d. DO min 9.0 ppm max 12 ppm
A.4
Standar Operasi
A.4.1 Tuliskan secara singkat/umum ( 510 poin) standar operasi umum
Pembenihan yang dilakukan
a.
b.
c.
d.
Pemeliharaan induk
penanganan telur
penanganan noupli
kultur pakan alami
Plankton dan artemia
e. pemeliharaan larva
f. panen dan distribusi
B. Ongrowing Unit
No
B.1
B.2
Pertanyaan
Jawaban
Memetakan atribut data
B.1.1 Parameter apa saja yang diukur a. Suhu
/dicatat
selama
proses b. Salinitas
pembesaran?
c. pH
d. DO
B.1.2 Berapa kisaran nilai minimum dan a. Suhu min 29⁰C max 32⁰C
maksimum untuk setiap parameter? b.Salinitas min 25 ppt max 33 ppt
c. pH min 7.5ppm max 8.5 ppm
d. DO min 4.7 ppm max 7 ppm
Sistem Pengkodean
B.2.1 Apakah ada pemberian kode pada c. Ada
kolam?
d. Tidak ada
(Lingkari yang sesuai)
B.2.2 Bagaimana format kodenya?
Pake kode abjad dan angka
B.2.3 Apakah ada pemberian kode pada
pemasok pakan?
(Lingkari yang sesuai)
B.2.4 Bagaimana format kodenya?
B.2.5 Siapa saja nama pemasok pakan?
c. Ada
d. Tidak ada
Sesuai size yang digunakan
a. Pt matahari sakti
b. pt Suri ani Pemuka
39
B.3
B.4
Pencatatan Panen
B.3.1 Data/parameter apa saja yang Suhu
dicatat/diukur saat panen?
Salinitas
pH
DO
B.3.2 Berapa kisaran nilai minimum dan a. Suhu min 19⁰C max 22⁰C
maksimum untuk setiap parameter? b. Salinitas min 29 ppt max 33 ppt
c. pH min 7.5 ppm max 8.5 ppm
d. DO min 9.0 ppm max 12 ppm
B.3.3 Apakah dilakukan pencampuran a. Ya
ikan saat proses pemanenan dari b. Tidak
tiap keramba?
(Lingkari yang sesuai)
Standar Operasi
B.4.1 Tuliskan secara singkat/umum ( 5- a. Persiapan jaring kja
10 poin) standar operasi umum b. persiapan peralatan dan pakan
pembesaran yang dilakukan
c. penebaran benih/bibit
d. Pemberian pakan
e. panen dan pengemasan
C. Processing Unit
No Pertanyaan
Jawaban
C.1 Memetakan atribut data
C.1.1 Parameter apa saja yang diukur a. Suhu
/dicatat selama proses pasca panen? b. Salinitas
c. pH
C.1.2 Berapa kisaran nilai minimum dan a. Suhu min 15⁰C max 18⁰C
maksimum untuk setiap parameter? b. Salinitas min 28 ppt max 31 ppt
c. pH min 7.5 ppm max 8.5 ppm
d. DO min 9.0 ppm max 12 ppm
C.2 Sistem Pengkodean
C.2.1 Apakah ada pemberian kode pada e. Ada
kemasan
f. Tidak ada
(Lingkari yang sesuai)
C.2.2 Bagaimana format kodenya?
Sesuai jenis dan sise udang yang
dikemas,
Tanggal pengemasan.
C.2.3 Apakah ada pemberian kode pada g. Ada
unit
cold h. Tidak ada
storage/refrigerator/freezer?
(Lingkari yang sesuai)
40
C.2.4 Bagaimana format kodenya?
C.3 Standar Operasi
C.3.1 Tuliskan secara singkat/umum ( 5- a. Persiapan panen
10 poin) standar operasi umum b. Persiapan es curah
pasca panen yang dilakukan
c. pengangkatan jaring
d. Proses panen
D. Cold Storage Unit
No Pertanyaan
D.1 Memetakan atribut data
D.1.1 Parameter apa saja yang diukur
/dicatat
selama
proses
penyimpanan
pada
cold
storage/freezer?
D.1.2 Berapa kisaran nilai minimum dan
maksimum
untuk
setiap
parameter?
D.2 Sistem Pengkodean
D.2.1 Apakah ada pemberian kode pada
retailer/restaurant/konsumen
akhir? (Lingkari yang sesuai)
D.2.1 Bagaimana format kodenya?
D.3 Standar Operasi
D.3.1 Tuliskan secara singkat/umum ( 510 poin) standar operasi umum
penyimpanan yang dilakukan
Jawaban
a.
b.
c.
a. Ada
b. Tidak ada
a. Persiapan pembekuan
b. penyusunan dicolstorage
c. peroses pembekuan
E. Retailer Unit
No Pertanyaan
E.1 Memetakan atribut data
E.1.1 Parameter apa saja yang diukur
/dicatat selama proses penjualan?
Jawaban
a. kondisi udang
b.
c.
E.1.2 Berapa kisaran nilai minimum dan Masih beku apa tidak
maksimum untuk setiap parameter?
E.2 Standar Operasi
E.2.1 Tuliskan secara singkat/umum (5-10 a. Promosi
poin) standar operasi umum b. Scaner
penjualan yang dilakukan
c. Pengemasan
d. Pendistribusian
41
Appendix 2 Documentation result from software application
1. Breeding Unit
Seed ID
Pond
Number
of Seed
B120220159
B180220151
B180220158
B190220151
B191220141
B191220142
B191220143
B191220144
B191220145
B200220157
B201120141
B220220152
B220220153
B220220154
B271120141
B271120142
B271120143
B291120141
S00006
S00007
S00004
S00010
S00003
S00004
S00006
S00007
S00008
S00005
S00001
S00005
S00002
S00009
S00002
S00003
S00005
S00002
1200000
1200000
120000
1200000
3000
3000
3000
3000
3000
12000000
1000
12000000
1200
1200
3000
3000
2000
1500
Feed
Supplier
ID
PP0001
PP0001
PP0001
PP0001
PP0001
SH0001
PP0002
PP0001
PP0002
Number
of Seed
Harvest
Date of
Harvest
Date of
Input
3000 19/12/2014
3000 19/12/2014
3000 19/12/2014
1000 29/11/2014
2990 28/11/2014
20/02/2015
19/02/2015
21/02/2015
19/02/2015
19/12/2014
19/12/2014
19/12/2014
19/12/2014
19/12/2014
20/02/2015
20/11/2014
19/02/2015
19/02/2015
19/02/2015
27/11/2014
27/11/2014
28/11/2014
05/11/2014
2. Ongrowing Unit
Seed
Pond
B191220143 S00003
B271120141 S00001
Rearing
Pond ID
1000 G00001
2990 G00002
Number of
Harvest
Number of Seed
Seed ID
Date of
Harvest
19/12/2015
28/11/2015
1000
2980
3. Processing Unit
Rearing
Pond ID
G00001
G00002
Line
Processing ID
L00002
L00001
Seed ID
B191220143
B271120141
Number
of Seed
Countainer ID
1000
C00002
C00001
2980
Tempe
rature
(°C)
24
25
pH
Salinity
(ppm)
7
7
32
32
4. Cold Storage Unit
Cold
Storage
ID
CS00001
Line
Processing ID
L00001
Truck ID
Number of
Countainer
Grade
E12345
30
A
Temperature
(°C)
10
pH
Date of
Expedition
7
32
42
Appendix 3 Fuzzy set formulation for Product Inventory
The buffer inventory of products which is defined as many as 7 tons.
Membership function for Fuzzy set of product inventory define based on Eq.6.
0
; P ≤ 1 or P ≥ 5


µ LOW [ P ] =  ( P − 1) / 2 ;
1≤ P ≤ 3
 (5 − P ) / 2 ;
3≤ P ≤5

0
; P ≤ 3 or P ≥ 5


µ MEDIUM [ P ] =  ( P − 3) / 2 ;
3≤ P ≤5
 (7 − P ) / 2 ;
5≤ P≤7

0
; P ≤ 5 or P ≥ 7


µ HIGH [ P ] =  ( P − 5) / 2 ;
5≤ P≤7
 (9 − P ) / 2 ;
7≤P≤9

43
Appendix 4 Fuzzy set formulation of product recall
Assumed that total batch production that have to recall is 20 tons, meanwhile
the probability true level of contamination is 0.2, that means the minimum amount
of product recall is 4 ton (Velthuis et al. 2009). The membership function shown
below:
0
; R ≤ 4 or R ≥ 16


µ LOW [ R ] =  ( R − 4) / 8 ;
4 ≤ R ≤ 12
 (16 − R ) / 4 ;
12 ≤ R ≤ 16

0
; R ≤ 12 or R ≥ 20


µ MEDIUM [ R ] =  ( R − 12) / 4 ;
12 ≤ R ≤ 16
 (20 − R ) / 4 ;
16 ≤ R ≤ 20

0
; R ≤ 16 or R ≥ 28


µ HIGH [ R ] =  ( R − 16) / 4 ;
16 ≤ R ≤ 20
 (28 − R ) / 8 ;
20 ≤ R ≤ 28

44
Appendix 5 Time required to perform several inspection process
No
1
Inspection parameters
Threshold
Time (Hour)**
Checking the production process parameters in each unit
Temperature
1/60
pH
1/60
Salinitty
1/60
DO
1/60
2 Observation the SOP
0.5
3 Checking of fish quality parameters through product samples (SNI 2000)
Microbial contamination
Max 5,0 x 105 colony/g
32
- TPC
Max <2 APM/g
28
- Escherichia coli
Negative APM/25 g
24
- Salmonella
Negative APM/25 g
28
- Vibrio cholerae
Max <3 APM/g
26
- Vibrio parahaemolitycus
Chemical contamination*
Max 0 µg/kg
20
- Chloramphenicol
Max 0 µg/kg
18
- Nitrofuran
Max 100 µg/kg
16
- Tetracycline
Physical
Maximum central temperature
0.12
Parasite
Max 0 Tail
20
32
Maximum time
* = when necessary; ** = Data random
45
Appendix 6 Fuzzy set formulation for inspection time
Assumed that checking (inspection) could be performed in parallel. Thus
time required to complete the inspection process in each unit based on the longest
time, which means 32 hours.
0
; I ≤ 16 or I ≥ 30


µ SHORT [ I ] =  ( I − 16) / 8 ;
16 ≤ I ≤ 24
 (30 − I ) / 6 ;
24 ≤ R ≤ 30

0
; I ≤ 24 or I ≥ 36


µ MEDIUM [ I ] =  ( I − 24) / 6 ;
24 ≤ I ≤ 30

30 ≤ R ≤ 36
 (36 − I ) / 6 ;
0
; I ≤ 30 or I ≥ 44


µ LONG [ I ] =  ( I − 30) / 6 ;
30 ≤ I ≤ 36
 (44 − I ) / 8 ;
36 ≤ R ≤ 44

46
Appendix 7 Fuzzy set formulation for total handling
Handling service time is assumed by a maximum lead time of production
which require 4 days or means 96 hour.
0
; H ≤ 16 or H ≥ 56


µ SHORT [ H ] =  ( H − 16) / 20 ;
16 ≤ H ≤ 36
 (56 − H ) / 20 ;
36 ≤ H ≤ 56

0
; H ≤ 36 or H ≥ 76


µ MEDIUM [ H ] =  ( H − 36) / 20 ;
36 ≤ H ≤ 56
 (76 − H ) / 20 ;
56 ≤ H ≤ 76

0
; H ≤ 56 or H ≥ 96


µ LONG [ H ] =  ( H − 56) / 20 ;
56 ≤ H ≤ 76
 (96 − H ) / 20 ;
76 ≤ H ≤ 96

47
Appendix 8 FAM rules of prediction handling time
Product Recall
Inspection time
1
Product
Inventory
LOW [3]
LOW [12]
SHORT [24]
SHORT [36]
2
LOW [3]
LOW [12]
MEDIUM [30]
MEDIUM [56]
3
LOW [12]
LONG [36]
LAMA [76]
4
LOW [3]
LOW [3]
MEDIUM [16]
SHORT [24]
MEDIUM [56]
5
LOW [3]
MEDIUM [16]
MEDIUM [30]
MEDIUM [56]
6
LOW [3]
MEDIUM [16]
LONG [36]
LONG [76]
7
LOW [3]
HIGH [20]
SHORT [24]
LONG [76]
8
LOW [3]
HIGH [20]
MEDIUM [30]
LONG [76]
9
LOW [3]
HIGH [20]
LONG [36]
LONG [76]
LOW [12]
SHORT [24]
SHORT [36]
11
MEDIUM [5]
MEDIUM [5]
LOW [12]
MEDIUM [30]
MEDIUM [56]
12
MEDIUM [5]
LOW [12]
LONG [36]
MEDIUM [56]
13
MEDIUM [5]
MEDIUM [16]
SHORT [24]
MEDIUM [56]
14
MEDIUM [5]
MEDIUM [16]
MEDIUM [30]
MEDIUM [56]
15
MEDIUM [5]
MEDIUM [16]
LONG [36]
MEDIUM [56]
16
MEDIUM [5]
HIGH [20]
SHORT [24]
MEDIUM [56]
17
MEDIUM [5]
HIGH [20]
MEDIUM [30]
MEDIUM [56]
18
MEDIUM [5]
HIGH [20]
LONG [36]
LONG [76]
19
HIGH [7]
LOW [12]
SHORT [24]
SHORT [36]
20
HIGH [7]
LOW [12]
MEDIUM [30]
SHORT [36]
21
HIGH [7]
LOW [12]
LONG [36]
SHORT [36]
22
HIGH [7]
MEDIUM [16]
SHORT [24]
SHORT [36]
23
HIGH [7]
MEDIUM [16]
MEDIUM [30]
MEDIUM [56]
24
HIGH [7]
MEDIUM [16]
LONG [36]
MEDIUM [56]
25
HIGH [7]
HIGH [20]
SHORT [24]
SHORT [36]
26
HIGH [7]
BANYAK [20]
MEDIUM [30]
MEDIUM [56]
27
HIGH [7]
BANYAK [20]
LONG [36]
LONG [76]
NO
10
Handling time
48
Appendix 9 Computation result of matrix M and B
1
0
 

 0
0
 0
0
 

 0
0


T
M 7 = A o B7 =  0 o (0 0 1) =  0
1
0
 

1
0
 0
0
 

 0
0
0 1

0 0
0 0

0 0
0 0 
0 1

0 1
0 0

0 0 
1
0
 

 0
0
 0
0
 

 0
0


T
M 6 = A o B6 =  0 o (0 0 1) =  0
1
0
 

 0
0
1
0
 

 0
0
0 1

0 0
0 0

0 0
0 0
0 1

0 0
0 1

0 0
 1
0
 

 0
0
 0
0
 

 0
0


T
M16 = A o B16 =  0 o (0 1 0) =  0
 1
0
 

 1
0
 0
0
 0
 0
 

1 0

0 0
0 0

0 0
0 0
1 0

1 0
0 0

0 0
 1
0
 

 0
0
 0
0
 

 0
0


T
M17 = A o B17 =  0 o (0 1 0) =  0
 1
0
 

 0
0
 1
0
 

 0
0
1 0

0 0
0 0

0 0
0 0
1 0

0 0
1 0

0 0
49
 0.5   0

 
 0.5   0
 0  0

 
 0  0
B7 ' = A'oM 7 =  0.875 o  0
 0  0

 
 0  0
 0.83   0

 
 0.17   0
0 1

0 0
0 0

0 0
0 0 = (0 0 0.875)
0 1

0 1
0 0

0 0
 0.5   0

 
 0.5   0
 0  0

 
 0  0
B8 ' = A'oM 89 =  0.875 o  0
 0  0

 
 0  0
 0.83   0

 
 0.17   0
0 1

0 0
0 0

0 0
0 0 = (0 0 0.875)
0 1

0 0
0 1

0 0
 0.5   0

 
 0.5   0
 0  0

 
 0  0
B16 ' = A'oM16 =  0.875 o  0
 0  0

 
 0  0
 0.83   0

 
 0.17   0
1 0

0 0
0 0

0 0
0 0 = (0 0.875 0)
1 0

1 0
0 0

0 0
 0.5   0

 
 0.5   0
 0  0

 
 0  0
B17 ' = A'oM17 =  0.875 o  0
 0  0

 
 0  0
 0.5   0

 
 0.5   0
1 0

0 0
0 0

0 0
0 0 = (0 0.875 0)
1 0

0 0
1 0

0 0
50
Appendix 10 Requirement verification matrix (RVM)
No Stakeholder
1
Login menu
2
Breeding
unit
3
Onrowing
unit
Business Proses
Status
Login menu to access input data interface in
each stakeholder
Setting user menu data
Inputting data attribute on breeding process :
- Seed ID
- Number of egg
- Seed pond ID
- Feed supplier ID
- Feed Name
- Temperature
- pH
- Salinity
- Date of hatchery
- Date of seed harvesting
- Date of input
- Time of input
- Operator name
Print out the seed label
Into barcode label and PDF file
Label contains :
- Seed ID
- Date of breeding
- Date of hatchery
Setting Number of seed pond and SOPs are
used
Generate breeding report into PDF file
Data included:
- Seed ID
- Pond ID
- Number of seed harvest
- Survival rate
- Feed supplier ID
- Feed name
Scan seed label
Information retrieval :
- Seed ID
- Seed pond ID
Inputting ongrowing data attribute:
- Seed ID
- Seed pond ID
- Rearing pond ID
- Feed supplier ID
- Feed Name
- Temperature
- pH
51
4
5
- Salinity
- Total harvest
- Date of seed spread
- Date of harvest
- Date of input
- Time of input
- Operator name
Setting Number of rearing pond and SOPs
are used
Generate ongrowing report into PDF file
Data included:
- Seed ID
- Pond ID
- Number of fish harvest
- Survival rate
- Feed supplier ID
- Feed Name
Processing
Scan seed Label
unit
Information retrieval:
- Seed ID Benih
- Rearing pond ID
Inputting data processing:
- Rearing pond ID
- Line ID3
- Number of product on each grade
- Packaging ID
- Temperature on post harvest
- Date of production
- Date input
- Time input
- Operator name
Print out packaging Label into barcode label
dan PDF file
Label contain :
Packaging ID
Date of production
Generate production report into PDF file,
that contain
- Seed ID
- Rearing pond ID
- Line ID
- Grade
- Number of unit
Cold Storage Scan the packaging label
unit
Information retrieval:
- Seed ID
- Line ID
52
6
Retailer unit
- Grade
- Number of packaging
Inputting cold storage data:
- Packaging ID
- Line ID
- cold Storage ID
- Temperature
- Date of entering the cold storage
Inputting expedition data
- Packaging ID
- Cold storage ID
- Truck ID
- Temperature
- Date of expeditioni
- Retailer ID
Setting number of cold storage and
expedition unit
Print mail road
Scan the packaging ID
Information retrieval:
- Truck ID
- Cold Storage ID
- Grade ID
Inputting retailer data :
- Packaging ID
- ID Truk
- Temperature
- Date of receiving
Inputting product sale data
- Packaging ID
- Date of sales
- Number of packaging
Tracing Product
- Scan packaging ID information that will
retrieve is all data from cold storage until
breeding unit
- Temperature
- Graphic temperature
- Flowchart of production process
- Similarity measurement
Print out the tracing report
53
Appendix 11 Sample of application form for data capturing on breeding unit
BREEDING UNIT
APPLICATION FORM
Date of Capture
:
:
Form ID
Operator Name
Data of Product
Seed ID
Number of egg
Date of birth
Pond Seed ID
Feed supplier ID
Feed name 1
Feed name 2
Feed name 3
Feed name 4
Feed name 5
:
:
:
:
:
:
:
:
:
:
Data of Processing
SOP
1
2
3
4
5
6
:
:
:
:
:
:
:
Checklist
Data of cultivation Quality parameter
Attribute
Temperature (°C)
pH
Salinity
:
:
:
:
Value
:
:
54
Appendix 12 Fragment of documentation process at ongrowing unit
Ongrowing Unit
55
Appendix 13 Fragment of documentation process at processing unit
Processing Unit
56
Appendix 14 Fragment of documentation process at cold storage unit
Cold Storage Unit
57
Appendix 15 Fragment of documentation process at retailer unit
Retailer Unit
58
BIOGRAPHY
Aditia Ginantaka was born on August 27, 1987 at Tabanan, Bali, Indonesia.
He is the second son of Mr. Wagiman, and Mrs. Enan Kesmanasari. He was studied
at Senior High School in Purwokerto and graduated in the mid 2005. He received
his Bachelor degree in Agro-industrial Technology from Bogor Agricultural
University in 2010.
He received a BPPDN-DIKTI scholarship to continue his studies and
completed his Master in Agro-Industrial Technology studies program, Graduate
School of Bogor Agricultural University. During his college, he was join as
community organizer of FORUM WACANA IPB (IPB graduate school student
forum) in part of public relation. He also as founder "Agro-Edu Community" which
is a social activity for educate poor children around Bogor district.
His current and previous research interests is about system engineering,
supply chain management and soft computing. Author has published a paper about
traceability system on International Conference on Advanced Computer Science
and Information System (ICACSIS 2014), which was held in Jakarta. He has
succeeded awarded as the best session presenter. Besides that, he have succeeded
to present his paper with the tittle An Optimization of Product Recall Cost for
Frozen Milkfish in Traceability System in International Conference on Mechanical,
Industrial and Manufacturing Technology (MIMT 2015) which was held in Malaka,
Malaysia during 6-7 March 2015