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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

A System Analysis and Design for Sorghum Based Nano–Composite Film Production

Abstract— The existence of process complexity and

higher inter–dependency within production system of biodegradable sorghum–based nanocomposite film are obviously required for system analysis and design. In this paper, nanocomposite film production was simulated in Business Process Modeling Notation (BPMN) model to assist researchers in decision making. The system was designed by using 2 processing variables as the model input, namely hydrolysis time and plasticizer ratio. Then by modifying and controlling value of these variables, researcher would determine their influences on 3 nanocomposite film crucial physical and thermal properties, such as Water Vapor Permeability (WVP), X–Ray Diffraction (XRD) and Derivative Thermo-gravimetric (DTG). Numerical examples were implemented in order to illustrate the related issues. Model verification result showed that the model succeeded conducting an improvement for all film properties compared to other various composites. Therefore this system is potentially possible to simulate sorghum–based nanocomposite film production in a simpler and fully controllable model.

Keywords— System analysis and design, Business Process Modeling and Notation (BPMN), sorghum biomass, nanocomposite film, biodegradable

I. INTRODUCTION

etroleum material that is generally used in packaging industry [1] nowadays have been

turning into serious constraints by its non-biodegradable property which is hazardous to surrounding environment [2]. Besides, plastic recycle problem and fossil materials extinction motivate researchers to develop some other sustainable, environmental friendly, new materials to substitute synthetics polymer and mineral fillers. In recent years, natural polymers such as cellulose have been attracting not only researchers but also industry stakeholders as potential filler alternative [2,3,4], because since it is biodegradable and renewable [5] cellulose has high tensile strength and rigidity [6,7], high reinforcing potency and wide surface area [8]

Cellulose is biopolymer that can be obtained from wood or non-wood resources. Non-wood substance likes agricultural residues is more preferable related to limitation in wood usages in last decade

. Therefore, cellulose as natural resources is highly reasonable to replace petroleum or other synthetic polymer.

[9,10]. Sorghum biomass, which is generally used as feed, is able to be developed while in order to enhancing its added value it can be converted into high performance substitute matter [11]. Cellulose fiber as filler and reinforcing agent has many advantages such as

specific surface area which can reaches hundreds m2/g, very high elasticity modulus (Young), high ratio aspect, significant reinforcing ability even in smaller filler content, low density, non-abrasive, non-toxic, high visible light transmission, can be easily modified chemically because its structure has of –OH side group reactive surface, biocompatible, biodegradable, renewable, high availability so it is cheap, and environmental-friendly [12]..

In order to conduct improvement and/or optimization of the process, a modeling is applied in a system simulation. Many modeling method and computing languages have been studied to embrace all perspectives which are necessary in model such as information, functions and behaviors as much as possible. Business Process Modeling Notation (BPMN) as example attracts business engineers because it can define proses workflow in simpler and more flexible way and facilitate the execution which is simulated automatically linked to Business Process Modeling System (BPMS)

Those are technical reasons that insist cellulose based nanocomposite promising potency of being substitution material in term of packaging application development. Meanwhile, those complexity is considered as barrier for researcher before conducting the process in laboratory. Modeling comes as solution for this problem because we can simulate the process and controlling the variables as well to find whether there will be any improvement or optimization to the product.

[13]

The reminder of the paper is organized as followed. In Section II, it provides clearer explanation about importance of determining variables used in this model, such as hydrolysis time and plasticizer ratio. It also tells the both general and specified objectives of modeling construction. In Section III, mathematical formulas applied in process are presented considering with model’s objectives. Modeling methods conducted are also interpreted. This section defines how the system is developed sequ9entially and results and discussion after the production system model designed and analyzed and the verification of it afterwards. Lastly, its conclusion is written in Section IV.

. Objective of this modeling using BPMN is to construct a simple and fully–controllable system presenting a real sorghum– based nanocomposite production process.

II. PROBLEM STATEMENT

In cellulose nanostructure preparation, acid hydrolysis is one of most popular techniques which is

P

Belladini Lovely and Taufik Djatna Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Indonesia

Email: [email protected], [email protected]

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able to break messy, separated and amorphous parts, and subsequently release it as well-blended single crystal component. Cellulose fiber was isolated from sorghum bagasse and was treated in sulfuric acid (H2SO4) in four varied hydrolysis times, 15, 30, 60 and 90 minutes, subsequently properties of Nano fiber produced were characterized. Afterwards, to construct Nano composite, Nano fiber was mixed into polyvinyl alcohol (PVA) with presentations of Nano fiber used were 0.5, 1 and 3 wt %. BPMN modeling of Nano composite properties characterization, consisting of XRD and WVP, aimed to evaluate the compatibility of Nano composite which its filler was made by sorghum bagasse cellulose Nano fiber related to influences of variables described before. General objective of the modeling was to courage improvement of Nano composite properties by presented the processing condition system into a designed BPMN model. To succeed this purpose, there were specified objectives such as analyzing and identifying the influences of: 1) varied hydrolysis time; and 2) varied plasticizer ratio for nano-composite film production. Indicators targeted in this process were nano-fiber size of 1 to 3.5 nm and improved nano-composite‘s XRD and WVP, if compared to nano-composites made by other resources and methods. Responding to the objectives, capability expected to be existed in the built system was ability to design improvement model of nano-composite film production with two determining variables, which have been described above, and then analyze it, therefore optimum value could be identified in order to reach the best film characteristics.

III. METHODOLOGY

A. Mathematical Formulation The system was formulated mathematically based on previous study. In former researches about nano-fiber based nano-composite, characterization of the sample is conducted to analyze whether the result shows an improvement compared to previous ones. Physical and thermal properties are ones of most important in film composite product assessment, such as Water Vapor Permeability (WVP), X-Ray Diffraction (XRD), and Derivative Thermo-Gravimetric (DTG).

A.1 Water Vapor Permeability (WVP) WVP is water resistance properties of nanocomposite film which roles in maintaining the shelf-life of packaged product.

(1) where x is film thickness (m), A is film surface area exposed to permeant (m2), P0 is vapor pressure of pure water (1753.55 Pa at 25 oC) and (RH1–RH2

A.2 X-Ray Diffraction (XRD)

) is relative humidity gradient used in experiment.

XRD is crystalinity index of nanocomposite film which roles in film structural stability. XRD is calculated in x-ray diffraction angle width of 5o to 30o

(2)

(2θ) with power of 20 kV and 2mA.

where I is diffraction intensity assigned to (200) plane of cellulose and I’ is intensity measured at 2θ – 18.

A.3 Derivative Thermo-Gravimetric (DTG) DTG shows degradation temperature of nanocomposite film which roles in packaging quality and storage application. Thermal stability of few amount (mg) of sample placed on aluminum glass was tested in nitrogen condition and heating rate of 10 o

B. System Modeling

C per minute.

Modeling stage role in identifying and decomposing business process so that system can be identified by detail and focused on control flow (sequence execution) or data flow (data exchange). In BPM, there are diagrams that relate each other in analyzing the model. Process Hierarchy Diagram (PHD) represents processes held in system into a hierarchy, where each process was analyzed one by one afterwards in Business Process Diagram (BPD). As addition, BPM also uses various languages representing its process, which one of them is analysis language that uses graphical notations standardized to illustrate control flow in Business Process Modeling Notation (BPMN). Required steps in analyzing and designing production system of nano-composite film made of sorghum biomass as its nano-filler are described below. The following sub-sections describes more in detail about each steps and diagrams built in the modeling. First the system was defined and then its needs and variables were analyzed. After that, the diagrams’ construction was started sequentially from Process Hierarchy Diagram (PHD), Business Process Diagram (BPD) until the last which was Business Process Modeling Notation (BPMN) [14]

B.1 System Definition

.

System is group of elements operated by integration, where each one has ability determined explicitly, that are able to work by synergic to process added values and offer users to satisfy their defined operational needs by result and success probability. System involves individual (organization), product and tools in several levels. In common, self-made system needs many levels of plans, operations, interventions and human resource support. System development is conducted if former system is no longer proper and compatible or able to meet the needs or updates occurred in the organization or firm. Development cycle or step in a system starts from planning, analysis, designing, implementation and maintenance, or well-known as System Development Life Cycle (SDLC) [15].

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B.2 Requirement Analysis System as a simple entity can be presented generally below. Necessary inputs are processed then generate outputs (products, by-products or services) aimed to solve problems and constraints. Meanwhile in analyst perspective, several critical information, which are describing how system operates, are gone. Those attributes are acceptable and unacceptable inputs, stakeholders, roles-missions-objectives, risks, controls, resources and acceptable and unacceptable outputs [15]

The important factor analysis was then solved by using RELIEF algorithm for its importance score by comparing them to the defined target. The higher score a factor of variable gets, the more important and crucial it is to the system. As ranking of variables’ importance was achieved, some of top factor would be chosen as the determining factors discussed in model result.

.

B.3 Process Hierarchy Diagram (PHD) The requirement for certain hierarchy structure implemented as a PHD. PHD is high level diagram that used to analyze business function as a hierarchy of process so it is also known as functional decomposition diagram. PHD illustrates graphically functions of the system and spreads them into many sub-processes.

B.4 Business Process Diagram (BPD) In order to support business process clarity, BPD analyzes how many sub-processes of the system built by PHD being allocated to people, organizations or groups. This diagram determines the control flow of process and helps user to figure how data goes through them. BPD also defines the data flow influences on sub-processes implementation.

B.5 Business Process Model and Notation (BPMN) PHD and BPD eventually lead to a BPMN. BPMN 2.0 version in this current work is standardized graphical notations so that is more easily to be understood not only by business stakeholders but also analyst and technical developers who have role in configuring and monitoring after the system implementation. BPMN 2.0 consists of diagrams such as: 1) Conversation diagram overviews communication that linked between stakeholders. 2) Choreography diagram defines details of conversation held between two or more stakeholders that are drawn in specific conversation nodes. 3) Collaboration diagram describes messages flowing through the stakeholders. 4) Process diagram explains sequence flow of each process own by stakeholders. In this modeling, choreography diagram is generally applied to illustrate the processing system by analyzing how each stakeholder switch and/or trade information and messages. The diagram coordinates the links by detail using the conversation nodes. Participants in the diagram represent firms, departments or divisions who have roles in system

collaboration into swim lane, while they have not any separated unique symbols but names are written above or below choreography task symbol. Diagram’s events are things happened in process and consisting of start, end, and intermediate activities, such as state and/or message changes, which are able to influence sequence and timing of model. Whereas another symbol in the diagram, gateways, controls the process’ sequence flow like blend or separate away the flows behind gateway condition. Activities of diagram are works or tasks conducted in whole processes. Data is physical information made, manipulated or used in process execution while messages show contents traded between two or more stakeholders, and run through the flows. Steps required to construct BPMN diagram are, in sequence, 1) determine departments involved in models and also each one’s stakeholders and lists of their roles, 2) represent process flows conducted, 3) apply available notations then rearrange the orchestration, 4) complete the diagram with explanations and data, connect behaviors in the interactions between stakeholders then build choreography collaboration diagram and, last, recheck the model if it has any errors or warnings until the output intends there is zero mistakes. Data implemented in order to execute the formulas was taken from literature, former researches which applied likely materials (soy, wheat, corn etc.) as Nano filler to manufacture the composite film. Cited articles, journals and reports chose the same variables to be observed each one’s influences on process output. It was proved before that the impacts of chosen determining variables to the product were significant by statistics experimental design.

IV. EXPERIMENTAL RESULTS

Input of the system were derived from previous studies data [16.17,18,19]

A. System Development

while some were hypothetical such as in variable analysis. Data were assumed by adjusting the range given in former researches’ result with the system needs. Subsequently, output were confronted with reference data which had been best so far, so that existence of improvement could be analyzed.

Definition and development of the system will be described below. SDLC described that idea, as the first element which began the system [15], was production of sorghum bagasse cellulose Nano fiber based Nano composite film as a biodegradable and environmental friendly alternative in food packaging with improved properties. By analysis, required humans/users were researchers/lab workers, analysis, stakeholders in corporations related to composite, sorghum waste handling, food packaging and nanotechnology products. Production system requirement was determined by considering complexity in hydrolysis time and mixture solvent of cellulose Nano fiber cellulose–PVA Nano composite,

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risk plan in them and stakeholders communication. Afterwards, model could be designed by using success indicators. Model built then evaluated and tested before it could be deployed in Nano composite researches development and it has to be noted that maintenance is necessary before it comes back again to the starting point of idea generation.

TABLE I VARIABLE ANALYSIS OF NANO-COMPOSITE PRODUCTION SYSTEM

T (o WVP C) XRD DTG Target 90 5.5 100 0 Δ 100 4 100 0.1 Δ 110 3.8 99 0.3 Ο 120 2.5 98 0.3 × 130 1.7 95 0.35 □ 140 0.7 95 0.4 □ 150 0.4 94 0.5 □

WVP = [ (-1.5+1.8+3+3.8+4.8+5.1) + (0.2+1.5+2.3+3.3+3.6) + (1.3+2.1+3.1+3.4) + (0.8+1.8+2.1) + (-1-1.3) + (-0.3) ] / [ 7 x (100 – 94) ] = 41.9 / 35.7 = 1.174 XRD = [ (0+1+2+5+5+6) + (1+2+5+5+6) + (1+4+4+5) + (3+3+4) + (0-1) + (-1) ] / [ 7 x (100 – 94) ] = 60 / 42 = 1.429 DTG = [ (-0.1+0.3+0.3+0.35+0.4+0.5) + (0.2+0.2+0.25+0.3 +0.4) + (0+0.05+0.1+0.2) + (0.05+0.1+0.2) + (-0.05 +0.15) + (-0.1) ] / [ 7 x (100 – 94) ] = 3.5 / 3.5 = 1

Based on analysis result in nano composite production system variables shown in Table I, ranks of variables’ importance could be achieved. In sequence, from the most important to the less important shown by each score, there were WVP of 1.174, XRD of 1.429 and DTG of 1. The two most crucial variables were chosen as determining variable in this model.

B. Model Analysis Result BPMN diagram designed in this production system consists of 3 divisions and each one is divided into 2 sub-divisions. First division is Isolation of biomass cellulose which branches into sub-division Biomass preparation/pre-treatment and Disk-milling. Second division is Cellulose Nano fiber production which branches into sub-division Cellulose hydrolysis and Nano-cellulose suspense dispersion. Last is Nano composite film production which branches into Film Formulation and Film Casting. There were critical tasks in diagram swim lane which define how the determining variables influence the process output. In Figure I, there is a division namely Hydrolyzing cellulose isolate where the impact of variable no.1, hydrolysis time, works. It was started by preparing the cellulose isolate. A start as starting point of the flow decomposes the processes [14]

. As shown in diagram, the start event used is message start of top-level. A message represents the content of a communication between two participants using a flow. First message flow showed input message of cellulose isolate that is came from former

participant in Disk-milling sub-division and afterward sent to the task receiving cellulose isolate.

Figure 1. BPMN Swim-Lane of Cellulose Hydrolysis

Message flow in the diagram continued to deliver cellulose isolate from preparation in start event to sub-process of hydrolysis. Sub-process is an activity whose internal details have been modeled using activities, gateways, events and sequence flows [14]

Another critical step in production system, film formulation, as same as it was in cellulose hydrolysis. It can be seen in Figure II the BPMN swim-lane of the process. Film production is the next division where variable no.2, plasticizer ratio, roles.

. Cellulose was mixed with sulfuric acid solution at first which defined as manual task. That manual icon means that the task is performed without aid of any business process execution engines or any other applications so it was held by human/operator self-hands. Next, there was script task, a task executed by script interpreted by a business process engine, namely stirring the mixture solvent using magnetic stirrer. It indicated that the task was conducted using engine power. It ended in end event by taking crude Nano cellulose suspension along in its message flow. Data association link showed the output information generated from this sub-process was gross volume of the suspense. The sequence flow steps forward to the next sub-process and divisions.

First, dispersed nano cellulose suspension was prepared by input message from former division. The suspense then continued to sub-process namely Nano cellulose-PVA Nano composite formulation. Suspense was mixed manually with PVA solution in various ratio of mixture. To make it well-dispersed, suspense was stirred using ultrasonic homogenizer. Mixture solution of film Nano composite formula then delivered to next division namely solvent casting to make the sheet of Nano composite film.

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Figure 2. BPMN Swim-Lane of Film Formulation

C. Model Verification Based on formula and reference data gathered

from previous studies [16.17,18,19]

Figure IV showed that top and middle line, as sample, compared to two lines below, as other commercial matrix, indicated higher crystalinity which is good to film stability. It verified that the model is able to deploy an improvement, in case of XRD value.

, the designed production system model was compared then its improvement was analyzed. Comparison result was presented in graphs and tables. Each picture below defined difference conducted between sample’s specific characteristics and reference data.

Figure V showed that a line, which is sample, compared to b line, as other commercial matrix, indicated greater point of degradation temperature which is good to packaging or storage application It verified that the model is able to deploy an improvement, in case of DTG value.

Figure VI showed that sample compared to other commercial matrix indicated lower permeability of water content which is good to maintain the shelf-life of packaged product. It verified that the model is able to deploy an improvement, in case of WVP value. Those outputs were verified as improvement model in case of the cellulose nano fiber and nano composite properties.

Figure 3. Comparison Graph of XRD

Figure 4. Comparison Graph of DTG

V. CONCLUSION

Complexity in sorghum cellulose nanofiber–polyvinyl alcohol (PVA) based nanocomposite production and massive inter-dependencies among each processing steps were required in system analysis and design. Model was designed requiring hydrolysis time and plasticizer ratio as the input. Model output was to find out if there would be any improvement on XRD, DTG and WVP properties, as ones of the most crucial nanocomposite characteristics in packaging application. PHD and BPD diagram were built before making BPMN as the system analysis and design main tool. In conclusion, model validation result showed nill error and warning. Afterwards, in BPMN verification, sample and other composite comparison determined that all three properties indicated improvement. XRD was defined as the most crucial among other properties, confirmed by its value (1.4) as the highest in RELIEF measurement. This model is expected to be applied by researchers to assist them developing various nano-composite products and also for other fields as well.

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Figure 5. Comparison Table of WVP

REFERENCES

[1] Shchipunov Y. 2012. Bionano-composites: Green sustainable materials for the nearfuture. Pure and Applied Chemistry, 84, 2579–2607

[2] Tang X, Kumar P, Alavi S, Sandeep K. 2012. Recent advances in biopolymersand biopolymer-based nano-composites for food packaging materials. CriticalReviews in Food Science and Nutrition, 52, 426–442.

[3] Gibson PW, Lee C, Ko F, Reneker D. 2007. Application of nano-fiber technol-ogy to nonwoven thermal insulation. Journal of Engineered Fibers and Fabrics, 2,32–40.Habibi Y, Lucia LA and Rojas OJ. 2010. Cellulose nanocrystals: chemistry, self-assembly and application. Chemical Reviews 110:3479-3500

[4] Mazalevska O, Struszczyk MH, Chrzanowski M, Kruci´nska I. 2011. Application of electrospinning for vascular graft performance–preliminary results.Fibres & Textiles in Eastern Europe, 87, 46–52.

[5] Chen W, Yu H, Liu Y, Chen P, Zhang M, Hai Y. 2011. Individualization ofcellulose nano-fibers from wood using high-intensity ultrasonication combined with chemical pretreatments. Carbohydrate Polymers, 83, 1804–1811.

[6] Zimmermann T, Bordeanu N, Strub E. 2010. Properties of nano-fibrillated cellulose from different raw materials and its reinforcement potential. Carbohydrate Polymers, 79(4), 1086–1093

[7] Afra E, Yousefi H, Hadilam MM, Nishino T. 2013. Comparative effectof mechanical beating and nanofibrillation of cellulose on paper proper-ties made from bagasse and softwood pulps. Carbohydrate Polymers, 97,725–730.

[8] Abraham E, Deepa B, Pothan LA, Jacob M, Thomas S, Cvelbar U. 2011. Extraction of nanocellulose fibrils from lignocellulosic fibres: A novel approach. Carbohydrate Polymers, 1468–1475.

[9] Villar JC, Revilla-Gómez N, Carbajo JM, Simón JL. 2009. Improving the useof kenaf for kraft pulping by using

mixtures of bast and core fibres. Industrial Crops and Products, 29, 301–307.

[10] Alila S, Besbes I, Rei-Vilar M, Mutjé P, Boufi S. 2012. Non-woody plants asraw materials for production of microfibrillated cellulose (MFC): A comparativestudy. Industrial Crops and Products, 41, 250–259.

[11] Alemdar A and Sain M. 2008. Isolation and characterization of nano-fibers from agricultural residue-wheat straw and soy hulls. Bioresources Technology 9:1664-1671

[12] Habibi Y, Lucia LA, Rojas OJ. 2010. Cellulose nanocrystals: chemistry, self assembly and application. Chemical Reviews 110:3479-3500

[13] Prades L, Romero F, Estruch A, Garcia-Dominguez A, Serrano J. 2013. Defining a Methodology to Design and Implement Business Process Models in BPMN according to the Standard ANSI/ISA-95 in a Manufacturing Enterprise. The Manufacturing Engineering Society International Conference, MESIC 2013. Universitat Jaume I, Spanyol.

[14] Alan K, David R, Webber, David RR. 2001. ebXML: The New Global Standard for Doing Business on the Internet. New Riders Publishing.

[15] Wasson CH. 2006. System analysis, design and developmet concepts, priciples and practices/ USA: John Wiley & Sons.

[16] Curvelo AAS, de Carvalho AJF, Agnelli JAM. 2001. Thermoplastic starch-cellulosic fibers composites: preliminary results. Carbohydrate Polymers 45, 183-188.

[17] Fahma F, Hori N, Iwata T, Takemura A. 2012. The morphology and properties of poly(methyl methacrylate)-cellulose nano-composites prepared by immersion precipitation method. Journal of Applied Polymer Science.

[18] Fahma F, Iwamoto S, Hori N, Iwata T, Takemura A. 2010. Isolation, preparation, and characterization of nano-fibers from oil palm empty-fruit-bunch (OPEFB). Cellulose. 17: 977-985.

[19] Ghaderi M, Mousavi M, Yousefi H, Labbafi M. 2014. All-cellulose nano-composite film made from bagasse cellulose nano-fibers for food packaging application. Carbohydrate Polymers. 104, 59-65

.

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