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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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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
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
ICACSIS 2014
International Conference on
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Hotel Ambhara, Jakarta
October 18th - 19th, 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:
Relative Density Estimation using Self-Organizing Maps
Denny
Page(s): 10-15
Abstract | Full Text:
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:
Government Knowledge Management System Analysis: Case Study Badan Kepegawaian Negara
Elin Cahyaningsih, lukman -, Dana Indra Sensuse
Page(s): 22-28
Abstract | Full Text:
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:
Hybrid Sampling for Multiclass Imbalanced Problem: Case Study of Students' Performance Prediction
Wanthanee Prachuabsupakij, Nuanwan Soonthornphisaj
Page(s): 35-40
Abstract | Full Text:
Audrey Bona, Jean-Marc Salotti
Page(s): 41-46
Abstract | Full Text:
Digital watermarking in audio for copyright protection
Hemis Mustapha, Boudraa Bachir
Page(s): 47-51
Abstract | Full Text:
Multi-Grid Transformation for Medical Image Registration
Porawat Visutsak
Page(s): 52-56
Abstract | Full Text:
Creating Bahasa Indonesian - Javanese Parallel Corpora Using Wikipedia Articles
Bayu Distiawan Trisedya
Page(s): 57-63
Abstract | Full Text:
An Extension of Petri Network for Multi-Agent System Representation
Pierre Sauvage
Page(s): 64-71
Abstract | Full Text:
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:
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:
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:
Quality Evaluation of Airline’s E-Commerce Website, A Case Study of AirAsia and Lion Air Websites
Page(s): 90-93
Abstract | Full Text:
A comparative study of sound sources separation by Independent Component Analysis and Binaural Model
Bagus Tris Atmaja
Page(s): 94-98
Abstract | Full Text:
Enhancing Reliability of Feature Modeling with Transforming Representation into Abstract Behavioral Specification (ABS)
Muhammad Irfan Fadhillah
Page(s): 99-104
Abstract | Full Text:
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:
Making Energy-saving Strategies: Using a Cue Offering Interface
Yasutaka Kishi, Kyoko Ito, Shogo Nishida
Page(s): 112-117
Abstract | Full Text:
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:
Extending V-model practices to support SRE to build Secure Web Application
Ala Ali Abdulrazeg
Page(s): 124-129
Abstract | Full Text:
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:
Implementation of Steganography using LSB with Encrypted and Compressed Text using TEA-LZW on Android
Ledya Novamizanti
Page(s): 138-143
Hotspot Clustering Using DBSCAN Algorithm and Shiny Web Framework
Karlina Khiyarin Nisa
Page(s): 144-147
Abstract | Full Text:
Framework Model of Sustainable Supply Chain Risk for Dairy Agroindustry Based on Knowledge Base
Winnie Septiani
Page(s): 148-154
Abstract | Full Text:
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ICACSIS 2014
International Conference on
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Hotel Ambhara, Jakarta
October 18th - 19th, 2014
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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
Audre
y 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
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
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 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
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Vina Ayumi
289-294
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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
B 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
International Conference on
Advanced Computer Science and Information System 2012
(ICACSIS 2014)
Hotel Ambhara, Jakarta
October 18th - 19th, 2014
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Contacts
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2014 International Conference on
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Ambhara Hotel, Jakarta
October 18
th-19
th, 2014
Published by:
Faculty of Computer Science
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]
ICACSIS 2014
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].
ICACSIS 2014
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.
TABLEI
VARIABLE ANALYSIS OF NANO-COMPOSITE PRODUCTION SYSTEM T (oC) WVP 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
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