icacsis 2014 international conference on advanced …

ICACSIS 2014

International Conference on Advanced Computer Science and Information System 2012 (ICACSIS 2014) Hotel Ambhara, Jakarta October 18th - 19th, 2014

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View Please enable Javascript on your browser to view all the page properly. Copyright Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promortional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from Faculty of Computer Science, Universitas Indonesia, Indonesia. Contacts ICACSIS Committee Email: [email protected] Phone: +62 21 786 3419 ext. 3225 Faculty of Computer Science, Universitas Indonesia Kampus UI Depok Indonesia - 16424 Phone: +62 21 786 3419 Fax: +62 21 786 3415 Email: [email protected]

Faculty of Computer Science - Universitas Indonesia ©2014

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Committee

Honorary Chairs A. Jain, Fellow IEEE, Michigan State University, US T. Fukuda, Fellow IEEE, Nagoya-Meijo University, JP M. Adriani, Universitas Indonesia, ID General Chairs E. K. Budiardjo, Universitas Indonesia, ID D.I. Sensuse, Universitas Indonesia, ID Z.A. Hasibuan, Universitas Indonesia, ID Program Chairs H.B. Santoso, Universitas Indonesia, ID W. Jatmiko, Universitas Indonesia, ID A. Buono, Institut Pertanian Bogor, ID D.E. Herwindiati, Universitas Tarumanagara, ID Section Chairs K. Wastuwibowo, IEEE Indonesia Section, ID Publication Chairs A. Wibisono, Universitas Indonesia, ID Program Committees A. Azurat, Universitas Indonesia, ID A. Fanar, Lembaga Ilmu Pengetahuan Indonesia, ID A. Kistijantoro, Institut Teknologi Bandung, ID A. Purwarianti, Institut Teknologi Bandung, ID A. Nugroho, PTIK BPPT, ID A. Srivihok, Kasetsart University, TH A. Arifin, Institut Teknologi Sepuluh Nopember, ID A.M. Arymurthy, Universitas Indonesia, ID A.N. Hidayanto, Universitas Indonesia, ID B. Wijaya, Universitas Indonesia, ID B. Yuwono, Universitas Indonesia, ID

B. Hardian, Universitas Indonesia, ID B. Purwandari, Universitas Indonesia, ID B.A. Nazief, Universitas Indonesia, ID B.H. Widjaja, Universitas Indonesia, ID Denny, Universitas Indonesia, ID D. Jana, Computer Society of India, IN E. Gaura, Coventry University, UK E. Seo, Sungkyunkwan University, KR F. Gaol, IEEE Indonesia Section, ID H. Manurung, Universitas Indonesia, ID H. Suhartanto, Universitas Indonesia, ID H. Sukoco, Institut Pertanian Bogor, ID I. Budi, Universitas Indonesia, ID I. Sitanggang, Institut Pertanian Bogor, ID I. Wasito, Universitas Indonesia, ID K. Sekiyama, Nagoya University, JP L. Stefanus, Universitas Indonesia, ID Marimin, Institut Pertanian Bogor, ID M.T. Suarez, De La Salle University, PH M. Fanany, Universitas Indonesia, ID M. Kyas, Freie Universitat Berlin, DE M. Nakajima, Nagoya University, JP M. Widyanto, Universitas Indonesia, ID M. Widjaja, PTIK BPPT, ID N. Maulidevi, Institut Teknologi Bandung, ID O. Sidek, Universiti Sains Malaysia, MY O. Lawanto, Utah State University, US P. Hitzler, Wright State University, US P. Mursanto, Universitas Indonesia, ID S. Bressan, National University of Singapore, SG S. Kuswadi, Institut Teknologi Sepuluh Nopember, ID S. Nomura, Nagaoka University of Technology, JP S. Yazid, Universitas Indonesia, ID T. Basaruddin, Universitas Indonesia, ID T. Hardjono, Massachusetts Institute of Technology, US T. Gunawan, Int. Islamic University Malaysia, MY T.A. Masoem, Universitas Indonesia, ID V. Allan, Utah State University, US W. Chutimaskul, King Mokut’s Univ. of Technology, TH W. Molnar, Public Research Center Henri Tudor, LU W. Nugroho, Universitas Indonesia, ID W. Prasetya, Universiteit Utrecht, NL W. Sediono, Int. Islamic University Malaysia, MY W. Susilo, University of Wollongong, AU W. Wibowo, Universitas Indonesia, ID X. Li, The University of Queensland, AU Y. Isal, Universitas Indonesia, ID Y. Sucahyo, Universitas Indonesia, ID

ICACSIS 2014



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Evaluation on People Aspect in Knowledge Management System Implementation: A Case Study of Bank Indonesia Putu Wuri Handayani Page(s): 1-9 Abstract | Full Text: PDF

Relative Density Estimation using Self-Organizing Maps Denny Page(s): 10-15 Abstract | Full Text: PDF

Multicore Computation of Tactical Integration System in the Maritime Patrol Aircraft using Intel Threading Building Block Muhammad Faris Fathoni, Bambang Sridadi Page(s): 16-21 Abstract | Full Text: PDF

Government Knowledge Management System Analysis: Case Study Badan Kepegawaian Negara Elin Cahyaningsih, lukman -, Dana Indra Sensuse Page(s): 22-28 Abstract | Full Text: PDF

Forecasting the Length of the Rainy Season Using Time Delay Neural Network Agus Buono, Muhammad Asyhar Agmalaro, Amalia Fitranty Almira Page(s): 29-34 Abstract | Full Text: PDF

Hybrid Sampling for Multiclass Imbalanced Problem: Case Study of Students' Performance Prediction Wanthanee Prachuabsupakij, Nuanwan Soonthornphisaj Page(s): 35-40 Abstract | Full Text: PDF

Interaction between users and buildings: results of a multicreteria analysis

Audrey Bona, Jean-Marc Salotti Page(s): 41-46 Abstract | Full Text: PDF

Digital watermarking in audio for copyright protection Hemis Mustapha, Boudraa Bachir Page(s): 47-51 Abstract | Full Text: PDF

Multi-Grid Transformation for Medical Image Registration Porawat Visutsak Page(s): 52-56 Abstract | Full Text: PDF

Creating Bahasa Indonesian - Javanese Parallel Corpora Using Wikipedia Articles Bayu Distiawan Trisedya Page(s): 57-63 Abstract | Full Text: PDF

An Extension of Petri Network for Multi-Agent System Representation Pierre Sauvage Page(s): 64-71 Abstract | Full Text: PDF

Gamified E-Learning Model Based on Community of Inquiry Andika Yudha Utomo, Afifa Amriani, Alham Fikri Aji, Fatin Rohmah Nur Wahidah, Kasiyah M. Junus Page(s): 72-78 Abstract | Full Text: PDF

Model Prediction for Accreditation of Public Junior High School in Bogor Using Spatial Decision Tree Endang Purnama Giri, Aniati Murni Arymurthy Page(s): 79-84 Abstract | Full Text: PDF

Application of Decision Tree Classifier for Single Nucleotide Polymorphism Discovery from Next-Generation Sequencing Data Muhammad Abrar Istiadi, Wisnu Ananta Kusuma, I Made Tasma Page(s): 85-89 Abstract | Full Text: PDF

Quality Evaluation of Airline’s E-Commerce Website, A Case Study of AirAsia and Lion Air Websites Farah Shafira Effendi, Ika Alfina

Page(s): 90-93 Abstract | Full Text: PDF

A comparative study of sound sources separation by Independent Component Analysis and Binaural Model Bagus Tris Atmaja Page(s): 94-98 Abstract | Full Text: PDF

Enhancing Reliability of Feature Modeling with Transforming Representation into Abstract Behavioral Specification (ABS) Muhammad Irfan Fadhillah Page(s): 99-104 Abstract | Full Text: PDF

Classification of Campus E-Complaint Documents using Directed Acyclic Graph Multi-Class SVM Based on Analytic Hierarchy Process Imam Cholissodin, Maya Kurniawati, Indriati, Issa Arwani Page(s): 105-111 Abstract | Full Text: PDF

Making Energy-saving Strategies: Using a Cue Offering Interface Yasutaka Kishi, Kyoko Ito, Shogo Nishida Page(s): 112-117 Abstract | Full Text: PDF

Knowledge Management System Development with Evaluation Method in Lesson Study Activity Murein Miksa Mardhia, Armein Z.R. Langi, Yoanes Bandung Page(s): 118-123 Abstract | Full Text: PDF

Extending V-model practices to support SRE to build Secure Web Application Ala Ali Abdulrazeg Page(s): 124-129 Abstract | Full Text: PDF

Shared Service in E-Government Sector: Case Study of Implementation in Developed Countries Ravika Hafizi, Suraya Miskon, Azizah Abdul Rahman Page(s): 130-137 Abstract | Full Text: PDF

Implementation of Steganography using LSB with Encrypted and Compressed Text using TEA-LZW on Android Ledya Novamizanti Page(s): 138-143 Abstract | Full Text: PDF

Hotspot Clustering Using DBSCAN Algorithm and Shiny Web Framework Karlina Khiyarin Nisa Page(s): 144-147 Abstract | Full Text: PDF

Framework Model of Sustainable Supply Chain Risk for Dairy Agroindustry Based on Knowledge Base Winnie Septiani Page(s): 148-154 Abstract | Full Text: PDF

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A

Achmad Benny Mutiara 467-471

Achmad Nizar Hidayanto 425-430

Adhi Kusnadi 171-176

Aditia Ginantaka 354-360

Afifa Amriani 72-78

Agus Buono 29-34

Agus Widodo 256-261

Ahmad Eries Antares 171-176

Ahmad Nizar Hidayanto 295-300

Ahmad Tamimi Fadhilah 269-276

Aini Suri Talita 467-471

Ajeng Anugrah Lestari 301-306

Ala Ali Abdulrazeg 124-129

Albertus Sulaiman 415-419

Alexander Agung Santoso Gunawan 237-240

Alfan Presekal 312-317

Alham Fikri Aji 72-78

Amalia Fitranty Almira 29-34

Anang Kurnia 342-347

Andika Yudha Utomo 72-78

Andreas Febrian 492-497

Aniati Murni Arymurthy 79-84, 216-221, 425-430

Anthony J.H. Simons 231-236

Anto S Nugroho 177-181

Arief Ramadhan 289-294

Arin Karlina 204-209

Ario Sunar Baskoro 227-230

Armein Z.R. Langi 118-123

Audrey Bona 41-46

Ayu Purwarianti 371-375

Aziz Rahmad 182-186

Azizah Abdul Rahman 130-137

Azrifirwan 388-393

B

Bagus Tris Atmaja 94-98

Bambang Sridadi 16-21

Bayu Distiawan Trisedya 57-63

Belawati Widjaja 256-261

Belladini Lovely 318-323

Bob Hardian 410-414

Boudraa Bachir 47-51

C

Chanin Wongyai 210-215

Cliffen Allen 376-381

D

Dana Indra Sensuse 22-28, 289-294

Darius Andana Haris 376-381, 438-445

Darmawan Baginda Napitupulu 420-424

Dean Apriana Ramadhan 382-387

Denny 10-15

Devi Fitrianah 425-430

Diah E. Herwindiati 431-437

Dwi Hendratmo Widyantoro 324-329

Dyah E. Herwindiati 450-454

E

Elfira Febriani 262-268

Elin Cahyaningsih 22-28

Endang Purnama Giri 79-84, 216-221

Enrico Budianto 492-497

Eri Prasetio Wibowo 467-471

Eric Punzalan 155-160

F

Fadhilah Syafria 336-341

Fajar Munichputranto 262-268

Fajri Koto 193-197

Farah Shafira Effendi 90-93

Faris Al Afif 484-491

Fatin Rohmah Nur Wahidah 72-78

Febriana Misdianti 330-335

Firman Ardiansyah 204-209

G

Gladhi Guarddin 312-317

H

Hamidillah Ajie 251-255

Harish Muhammad Nazief 312-317

Harry Budi Santoso 402-409

Hemis Mustapha 47-51

Herman Tolle 472-477

Heru Sukoco 367-370

Husnul Khotimah 461-466

I

I Made Tasma 85-89

Ida Bagus Putu Peradnya Dinata 410-414

Ika Alfina 90-93

Ikhsanul Habibie 361-366, 492-497

Ikhwana Elfitri 307-311

Imaduddin Amin 324-329

Imam Cholissodin 105-111

Imas Sukaesih Sitanggang 166-170

Indra Budi 256-261

Indriati 105-111

Irsyad Satria 342-347

Issa Arwani 105-111

Ito Wasito 446-449

Iwan Aang Soenandi 283-288

J

Janson Hendryli 431-437

Jean-Marc Salotti 41-46

Jeanny Pragantha 376-381

Joel Ilao 155-160

John Derrick 231-236

Junaidy Budi Sanger 367-370

K

Karlina Khiyarin Nisa 144-147

Kasiyah M. Junus 72-78

Kyoko Ito 112-117

L

Lailan Sahrina Hasibuan 222-226

Ledya Novamizanti 138-143

M

M Anwar Ma'sum 394-401

M. Anwar Ma'sum 484-491, 492-497

M. Iqbal Tawakal 484-491

Maria Ulfah Siregar 231-236

Maya Kurniawati 105-111

Meidy Layooari 177-181

Mira Suryani 402-409

Mohammad Uliniansyah 177-181

Muhammad Abrar Istiadi 85-89

Muhammad Asyhar Agmalaro 29-34

Muhammad Faris Fathoni 16-21

Muhammad Iqbal 467-471

Muhammad Irfan Fadhillah 99-104

Muhammad Octaviano Pratama 289-294

Muhammad Rifki Shihab 295-300, 301-306, 330-335

Muhammad Sakti Alvissalim 198-203

Murein Miksa Mardhia 118-123

N

Ni Made Satvika Iswari 171-176

Nina Hairiyah 262-268

Nuanwan Soonthornphisaj 35-40

Nursidik Heru Praptono 425-430

P

Pauzi Ibrahim Nainggolan 161-165

Pierre Sauvage 64-71

Porawat Visutsak 52-56

Prane Mariel Ong 155-160

Prasetia Putra 251-255

Putu Satwika 492-497

Putu Wuri Handayani 1-9

R

Ralph Vincent Javellana Regalado 246-250

Ravika Hafizi 130-137

Reggio N Hartono 177-181

Riva Aktivia 455-460

Roger Luis Uy 155-160

S

Sani M. Isa 431-437, 450-454

Satyanto Saptomo 367-370

Setia Damawan Afandi 187-192

Shogo Nishida 112-117

Sigit Prasetyo 348-353

Siobhan North 231-236

Sri Tiatri 498-504

Sri Wahyuni 295-300

Stanley Karouw 277-282

Stewart Sentanoe 177-181

Suraya Miskon 130-137

Syandra 478-483

T

Taufik Djatna 262-268, 283-288, 318-323, 354-360, 388-393, 455-460, 461-466

Teny Handayani 446-449

Tji beng Jap 498-504

Tonny Adhi Sabastian 312-317

V

Vina Ayumi 289-294

W

Wanthanee Prachuabsupakij 35-40

Widodo Widodo 251-255

Wilson Fonda 371-375

Wina 450-454

Winnie Septiani 148-154

Wisnu Ananta Kusuma 85-89

Wisnu Jatmiko 484-491

Y

YB Dwi Setianto 241-245

Yani Nurhadryani 342-347, 455-460, 461-466

Yasutaka Kishi 112-117

Yaumil Miss Khoiriyah 166-170

Yoanes Bandung 118-123

Yudho Giri Sucahyo 348-353

Yustina Retno W. Utami 241-245

Z

Zainal A. Hasibuan 402-409

lukman - 22-28


ICACSIS 2014



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View Please enable Javascript on your browser to view all the page properly. Copyright Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promortional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from Faculty of Computer Science, Universitas Indonesia, Indonesia. Contacts ICACSIS Committee Email: [email protected] Phone: +62 21 786 3419 ext. 3225 Faculty of Computer Science, Universitas Indonesia Kampus UI Depok Indonesia - 16424 Phone: +62 21 786 3419 Fax: +62 21 786 3415 Email: [email protected]


2014 International Conference on

Advanced Computer Science and Information Systems

(Proceedings)

Ambhara Hotel, Jakarta

October 18th-19th , 2014

Published by:

Faculty of Computer Science

Universitas Indonesia

An Analysis and Design of Frozen Shrimp Traceability System Based on

Digital Business Ecosystem

Abstract—Traceability system is one of the most critical requirements in logistic information systems and the supply chain risk management for both global food safety and quality assurance. Real-time documentation from the earlier stages of production process enabled the two way process of traceability. This paper presented an analysis and design for traceability system of frozen Vanname shrimp based on digital business ecosystems (DBE) model. Business Process Model Notation (BPMN 2.0) was the primary tool in analyzing the task for capturing and transferring data processing between traceable units in each layer of DBE. Business process analysis helped to understand the capturing steps as the main element within such traceability system. The results of the analysis showcased how traceability system work in digital business ecosystem which involved on dispersed stakeholders. Manual data transformation to the digital system was provided by stakeholders using digital species metaphors. The requirement for factor analysis was computed with Relief method to select the most important attribute to capture. Our evaluation showed that the proposed system was able for estimating water salinity and related hatchery parameters changing, such as broodstock ID which utilized as key code. Current results showed the readiness of application to transfer into real world operation. Keywords-traceability, digital business ecosystem, traceable unit, digital species

I. INTRODUCTION Traceability is the ability to verify the history,

location, or application of an item by means of documented recorded identification. Other common definitions include the capability (and implementation) of keeping track of a given set or type of information to a given degree, or the ability to chronologically interrelate uniquely identifiable entities in a way that is verifiable [1].

Traceability system could use to solve the food safety problems. The food safety context related to the efficacy of comestibles that can become the cause of diseases (bacteria, viruses, and germs) from one country to others will thus harm people’s health when consumed. This condition risked the potency of rejection from importer countries. Several regulations that control food safety give requirement to the

producers of comestibles to create a traceability system. This system is used to trace and track the flow of products on every supply chain mechanism in the production and its distribution processes.

The traceability system offers some benefits particularly in products with fresh and easily damaged characteristics such as vegetables, fruit, meat, milk and fish [2]. One of the efficacies of this system is that it can improve security towards food safety from the products it produced. This system enables elimination towards unqualified food products from the supply chain system as well as monitoring the environment condition that can influence the safety of products. Product certification gives significant influence in costumer confidence. Thus, traceability system is one of the requirements in ISO standard. ISO 22005 is one of the documents that regulated the design and implementation of traceability system in a feed and food supply chain [3].

Shrimp is one of the leading exports of fishery commodities. Indonesian export volume data show that 122 tons shrimp has been exported in 2012 [4]. The shrimp company has to meet the criteria in ISO standard which is regulated the traceability system to get the confidence of importers, therefore the rejection can be avoided. The traceability system is established by documenting exceptional information on every point of supply chain and process stages of product handling, thus food producers are able to give detail information on the food products. The information of the product collected by supply chain actor, then shared to another stakeholders. This process just happen when the actor become the member of business ecosystem in shrimp production. Therefore, it is required to model the process of documenting information on every point of supply chain which catalyzed by ICTs infrastructure. Several researchers have been conducted to find out the implementation of the traceability system, such as in vegetables supply chain [1], on soya beans [5] and in supply chain of aquaculture products [7].

The utilization of digital technology has formed a digital ecosystem in its business activity and therefore called as Digital Business Ecosystem (DBE). The data of documentation results can be stored in a database hence query can be conducted for the search process.

The development of traceability system on DBE base replaces all process documentation by using paper (paper based), thus resulting paperless document which benefits in the improvement in

Taufik Djatna and Aditia Ginantaka Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Indonesia

[email protected], [email protected]

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overcoming easily damaged products by recording the tracks of product quality, the improvement in product management recall, the automatic scanning, the improvement in stock management, and the decrease in both work force operational cost. The pilots traceability system based on DBE have been implemented in a private sector company which located in some sparsed Island. Their main business was on hatchery and processed food. Frozen Vanname shrimp chosen as the model because the complete process of the supply chain, start from breeding unit until the retailer or restaurant. This system’s constructions were in terms of interest a company which located in some sparse location of the Indonesian archipelago. Each actor in the traceability system is a model of organism in Digital Business Ecosystem which interact each other in data capture and tracing process. Through the development of Digital Business Ecosystem, 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 (Fig.1).

Fig 1.Structural Coupling between supply chain

ecosystem and digital ecosystem in traceability system [6]

The objective of this work were to analyze the requirement and to design of traceability system. We then focus to the proposed system for frozen Vanname shrimp products and then verify and validate the traceability system to evaluate system performance.

I. REQUIREMENT ANALYSIS

A. Business Process Analysis 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 [8]. 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 [9]. Based on the business process analysis then retrieved five

stakeholders who take a role in the system of traceability and divided into four structure systems.

1. Input This system requires data related to product, processes and product quality as main input [10]. Data related to product include its product identity code along 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 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. 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. The process is then continued by printing the report in the form of label contains barcode of seed ID and destination pool for the process of enlargement shrimp larvae. 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 [11]. Quantitative model in the process of documentation of the data was then factorized using Relief method. This analysis was performed to find out the most influential data attribute in traceable unit. There were some attribute data defined as variables which were analyzed by using Relief algorithm such as survive rates, average pond water temperature, pH of water and water salinity. That variable used as quality process parameter in seed production, thus the variable have to documented for complete information. Different probability of the attribute X data value calculated as follow [12]:

[ ] ( )( )

'1

equal

samecl samecl

p Gini XW X

p p×

=−

(1)

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355

Where,

( ) ( )( )

( ) ( )2

222' C | V

x X C CV

P VGini X P P C

P V∈

= × −

∑ ∑ ∑∑

W [X] is an approximation of the following difference of probabilities of attribute X, Pequal is P(equal value of X), Psamecl

As a system, traceability must fulfill basic architecture of Input-Process-Output components. The design of data flow which transforms inputs into outputs is represented in Fig. 2.

is P(same class), P(V) is probability of value, and P(C) is probable classes, P(C|V) is probability X value occurred in a certain class C.

Fig 2.The design of the data flow that transforms inputs and outputs

4. Output The data set will store in the traceability repository include the relevant traceability data generated during the company operations [7]. Every single data will distributed to the query application by getting input product code from customer. The retailer as one of end user will get traceability reports after the process. This report answers the following typical traceability questions for instance: - Result of tracing data product - Generated recall list which contain all the

needed information to contact affected customers and allow them to pull appropriate products if indicated

- Report which identifies any of the suspect lots or unit process with nonstandard procedure

II. COMPUTATIONAL EXPERIMENT A computational experiment was set up to verify

and validate at what extend the proposed system could fulfill the performance stakeholders required. A Java based application system in both PC-Windows 7 and Android-JellyBeans Machines was then constructed. The details are as follows

A. Capturing Data

Every stakeholder carried out documenting process into data input application installed on desktop computer in each stakeholder unit. As an example, in the unit of seeds provision, the documented data attribute among others were broodstock ID, seeds ID, provided feed mill supplier, water temperature of the seeds pond, pH and water salinity. The entire data attribute was documented during the activity of seeds handling process in the data application form on breeding unit. Figure of business process stage of seeds documenting data can be seen on Fig 3.

Fig 3. Business cycle of seeds data documenting

process

The stage of seeds handling process ended when the seeds grow turning into larva that are ready to be transferred into raising pond in the cultivation unit. After the entire data was documented, data was inputed from the seed data form into the desktop computer. Data input application in desktop computer would save all documented data, then printed the report in the form of label with barcode from seeds ID and pond destination for the process of raising the fish larva. Data input application were illustrated in Fig 4.

(2)

System Environment

Regulation Digital Device

Traceability System

Input: Data Product

Data Process

Stakeholder: - Breeding unit - Ongrowing unit - Processing unit - etc

Role: - Capture data - Tracing process

Output: Tracing report

Recall Report

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Fig 4. Application of seed data input

Every process of data input was carried out at the end of material handling process because documenting data related to production process and track record was carried out to the next stage during the material handling process. Every stakeholder carried out the same process namely data input process into desktop computers to be stored in a server. The printed barcode in label became the key for product registration handled by using barcode scanner (Fig 5). Application for other stakeholders was deployed input documented data in the next process of production.

Fig 5. Stage of data reading with barcode scanner

Table I shows data in the stakeholder of seeds supply unit documented during the ongoing system. Data attributes were analyzed by using relief method to figure out the most influenced factor and required to be well documented during the process of product

handling. The analyzed data attribute were numerical type showing a value. The results of factor analysis showed attribute sequence that influence the system and become the consideration in determining critical data attribute that were needed to be constantly documented. The attribute determination became a source to create security system in source code input data software so that the process of data input into server was not available until data attribute filled in. Table II shows example of relief method utilization to determine critical attribute.

TABLE II

RESULTS OF FACTOR ANALYSIS WITH RELIEFF METHOD

Data Attribute Relief Value Rank Survive Rate -0,4 4 Average Pond water Temperature

-0,1 2

pH of water -0,3 3 Water Salinity -0,093 1

B. Tracing data A database for this traceability information system

was constructed for data query using three mathematic model follow as manual tracing process, such as sorting, searching and check the suitable data with similarity measurement, then arrange as source code software by using java programming. This data query application would be used by end user as one of the Stakeholders roles for tracing distributed products in customers. Figure of product tracing process at end user stakeholder is shown on Fig 6.

In this explanation, the writer described the use of the third mathematic model for the process of product tracing by using data at the stage of seeds supply. Computing stage from search mode in data query application was initiated by location searching of date data by utilizing interpolation search method with the following equation:

[ ][ ] [ ] ( )BC Sought-BC

Position = DI -DI DI BC -BC

lowhigh low low

high low× +

Where BC is a broodstock code, DI is a data index, BC [high] is the top level of BC and BC [low] is bottom level of BC.

(3)

TABLE I DATA SEEDS DOCUMENTATION ON BREEDING FARM

No Index

Date

Batch number

Broodstock Seeds ID

Feed Supplier ID

Feed Supplier

ID Ongrowing

Pond

Survive Rates (%)

Pond Temperature

(°C)

pH of Water

Water Salinity (mg/l) code ID Code No

0 12/08/2014 1 IU 2 31 P 003 Jordan 1A 97 23 7 1212 2 IU 6 32 P 001 Simon 2A 97 23 7 1431 3 IU 10 33 P 001 Simon 1B 96 23 7 1308 4 IU 8 34 P 003 Jordan 3A 95 24 8 1310

1 13/08/2014 5 IU 4 35 P 003 Jordan 2B 97 25 7 1301 6 IU 9 36 P 001 Simon 2C 97 24 7 1403 7 IU 7 37 P 003 Jordan 3B 97 23 9 1100 8 IU 1 38 P 001 Simon 1C 96 24 9 1379

2 14/08/2014 9 IU 5 39 P 003 Jordan 1D 97 25 7 1159 10 IU 3 40 P 003 Jordan 4A 97 24 9 1191

Note: This data follow the normal distribution random hypothetical.

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Data Index of location search from Table I data uses equation model (3) was simulated in the following calculation:

Search key : 13 Data Index low : 0 Data Index high : 2

Position = 13–1214–12

𝑥(2 – 0) + 0 = 1 Data discovered in index [1] dated 13/08/2014 along with other data in Tabel III.

TABLE III DATA BASED ON TABLE I

No Index

Date

Broodstock ID seeds ID

… Water Salinity (mg/l) Code no

1 13/01/2014 IU 4 35 1301 13/01/2014 IU 9 36 1403 13/01/2014 IU 7 37 1100 13/01/2014 IU 1 38 1379

Based on Table I

The computing process by application enters the next stage based on broodstock ID. Before the process of data search was conducted, the broodstock ID was processed by using insertion method with the following stages:

- When i =1, x equals to Data [1] = 9 and j=0. Because data [0] = 4 and 9 > 4 thus process was continued for i=2.

- When i =2, x = Data [2] = 7 and j=1. Because data [1] = 9 and 7 < 9, thus carried out shifting to the left until data which was smaller than 7 was discovered. Results of this shifting, Data [1] = 7 and Data [2] = 9 whilst Data [0] = x = 4.

- When i =3, x = Data [3] = 1 and =2. Because Data [2] = 9 and 1 < 9, thus shifting was carried out until data which was smaller than 1 was discovered. Results of this shifting, Data [2] = 7 and Data [3] = 9 whilst Data [1] = x = 4. And so forth

Fig 6. Stage of product tracing by end user

The searched broodstock ID was IU7. However, the code identification used in search process was numerical data. Index number was the number of data position in database arrangement. The search process was explained as follows:

Search key : 7 Data Index low : 0 Data Index high : 3

Position = 7–19–1

𝑥 (3 – 0) + 0 = 2 Data discovered in index [2] seed ID 37 along with other data attribute among others.

TABLE IV DATA BASED ON TABLE I

Seeds ID

Feed Supplier ID

Feed Supplier

… Water Salinity (mg/l)

37 P003 Jordan 1100 (Based on Table 1)

After the searched data were attained, hence similarity measurement was carried out with standard data attribute using Cosine Similarity [13] as follows:

( ) ., ,|| |||| y ||

tx ysim x yx

=

Where x and y is two vectors for comparison, then ||x|| is the Euclidean norm of vector x = (x1, x2, x3….xp

2 2 21 2 ... px x x+ + +

) which defined as:

Standard data attribute in the form of numeric

value and factual data of search results is shown in Table V.

TABLE V

STANDARD DATA ATTRIBUTE OF QUALITY REFERENCE Attribute Symbol Survive

rate

Average Temp (°C)

pH of Water

Water Salinity (mg/l)

standard X1 98 25 8 1300 37 X2 97 23 9 1100

Suppose that X1 and X2 are the first two term -

frequency vectors in Table 4. That is, X1 = (98, 25, 8, 1300) and X2

Based on the results of similarity measurement, it indicated that the parameter of seeds quality from the searched seeds ID was almost similar to the standard parameter with value of 0,999. Thus, it could claim that the seeds ID were in the standard of cultivation process.

= (97, 23, 9, 1100). How similar are X1 and X2? Using Eq. (4) and (5) to compute the cosine similarity between the two vectors.

The dataset from ongrowing unit show in Table VI. Pond ID was inter-correlated with data attribute from ongrowing process, such as feed supplier ID and other quality process for instance pH water, temperature of water, survive rates, weight and container temperature. 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. The processing

(4)

(5)

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stage followed the same documentation process to get information about production line of shrimp packaging and then transport to the cold storage. The dataset of processing unit showed on Table VII.

In the same case, the data were available in order to get further information about the product. The identification of data utilizes by date other ID number which get from supply chain event.

Traceability management information system provided intangible advantages for instance practicality, security and the deliverability of data for each stakeholder. The data could save, organized and emerged easier. Besides financial benefits, this system given improvement in the quality of information for management decision-making, and prevents in errors documentation processes. The operator could save the data on the data base system just by complete all field on the data software application. Each data saved with high security from lost and easy transferring to the other stakeholder by the local network among the computer. This system could provide information for increasing

customer confidence about the frozen shrimp product. Some information included in the tracing report

such as: 1. Description of the product 2. Expired date of product 3. Flow chart of the process, include :

- Identification number of unit process or material

- Temperature process and water pond salinity

4. Similarity value of the process with standard procedure and

5. Duration of tracing

Other report can be produced to give information about recall list with contact number of affected customers. The result of reporting can be seen on Fig 7.

Fig 7. Report of tracing product

III. CONCLUSIONS

Based on the analysis using BPMN 2.0, it is known that there were five stakeholders taking role in traceability system. The results of critical attribute determination by using relief method determined that water salinity parameter became critical attribute that requires documentation. The results of mathematic verification model show that the model used was able to produce the expected parameter according to its purpose. The rule of sorting method could show data sorting process. Search method also proved that it could be utilized for searching data location index. Similarity measurement shows that data attributed similar with standard process, thus the value almost 1

IV. REFERENCES [1] Hu Jinyou, Zhang Xu, Liliana M.M Neculita M. Modeling

and Implementation of The Vegetable Supply Chain Traceability System. Journal of Food Control 30, 2013, 341–353.

[2] Scheer F.P. Optimising Supply Chain Using Traceability System. Wageningen University and Research Centre, The Netherland, 2006.

[3] [ISO] .ISO 22005:2007. Traceability in The Feed and Food Chain —General Principles and Basic Requirements for System Design and Implementation. New Delhi: Bureau of Indian Standard, 2007.

TABEL VI DATA SHRIMP DOCUMENTATION FROM ONGROWING UNIT

Date Pond ID

Harvest Container ID

Feed supplier ID

Feed supplier pH water

water temperature

(°C)

Survive Rate (%)

Weight (g)

Container Temperature

(°C) 12102014 2A HC2A01 FS003 PT DevFeed 8 26 98 16160 7

HC2A02 HC2A03 1A HC1A01 FS002 PT Comfeed 9 24 99 16780 7 HC1A02 HC1A03

Note: This data follow the normal distribution random hypothetical

TABEL VII DATA SHRIMP DOCUMENTATION FROM PROCESSING UNIT

Date packaging

Line ID

Pond ID

Cold Storage ID Grade (Box) @ 1000 g

Pack Temperature

(°C)

Cold Storage

Temperature (°C) A B C

12102014 L1 2A CS01 6 5 5 5 2

2C 8 3 5 5 2

1B 6 9 2 5 2 L2 1A 3 5 8 5 2 3A CS02 6 6 2 5 2 1C 4 4 6 5 2

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[4] [BPS]. Trends of Selected Socio-Economic Indicators of Indonesia. Statistics Indonesia, Jakarta, 2013.

[5] Thakur Maitri and Donelly Kathryn A.M. Modeling Traceability Information in Soy Bean Value Chains. Journal of Food Engineering 99, 2010, 98–105.

[6] Nachira F, Dini P, Nicolai A, Marion L.Louarn, Leon R.L (Ed). Digital Business Ecosystems. European Commission: Luxembourg, 2007, 1-20.

[7] Marchante A.P, Melcon A.A, Trebar M. and Filippin P. Advance Traceability System in Aquaculture Supply Chain. Journal of Food Engineering 122, 2014, 99–109.

[8] White Stephen A. and Miers Derek. BPMN Modelling and Reference Guide. USA: Future Strategies,Inc, 2008.

[9] Wasson C.S. System Analysis, Design and Development. New Jersey (NJ): John Wiley & sons, Inc, 2006.

[10] Thakur Maitri, Sorensen C.F, Bjornson F.O, Foras E, Hurburgh C.R. Managing Food Traceability Information Using EPCIS Framework. Journal of Food Engineering 103, 2011, 417–433.

[11] Derrick S and Dillon M. A Guide to Traceability Within The Fish Industry. 2004. Swiss: SIPPO/ EUROFISH, 2004.

[12] Kononenko Igor. Estimating Attributes: Analysis and

Extentions of Relief. European Conference on Machine Learning Catania, Italy, 1994, 171-182.

[13] Han Jiawei, Kamber Micheline, and Pei Jian. Data Mining Concepts and Techniques 2nd Edition. Morgan Kaufmann Publisher, 2006

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