Analisis dan Desain Sistem Traceability Produk Udang Beku Berbasis 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unpublished papers are stated clearly in the text as well as in the references.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 Eng Taufik Djatna, STP, MSi
Chairman

Dr Irvan Faizal, MEng
Member

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)

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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

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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

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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

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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 betwee