CONTENTS

WELCOME MESSAGE FROM GENERAL CHAIR 2

ABOUT IPB UNIVERSITY 3

ABOUT DEPARTMENT OF COMPUTER SCIENCE, IPB UNIVERSITY 5

COMMITTEES 6 Steering Committee 6 Conference Committee 6 Technical Program Committee 7 Reviewers 7

CONFERENCE SCHEDULE 10

VIRTUAL CONFERENCE GUIDELINES 12 General Information 12 For Paper Presenter 13 For Attendees 15 For Keynote/Invited Speakers 16 Emergency Contacts and Assistance 17

INVITED SPEAKERS 18

TECHNICAL PARALLEL SESSION SCHEDULE 20

Appendix A. Using Webex in Virtual Conference 25

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WELCOME MESSAGE FROM GENERAL CHAIR

On behalf of the Organizing and Program Committee, it is my great pleasure to welcome you all to the 1st International Conference in Computer Science and Its Application in Agriculture (ICOSICA) 2020 on 17th September 2020. The 1st ICOSICA 2020 is organized by the Department of Computer Science, IPB University, in association with the IEEE Indonesia Section. This is the first conference that brings together all the researchers in computer science and its applications in agriculture to be held by IPB University internationally. In this conference, the researchers share and present the latest research, issues, and recent developments in the application of computer science in agriculture.

The technical program of the conference consists of five keynotes and four tracks. The tracks are (1) Software Engineering and Information Science, (2) Computational Intelligence and Optimization, (3) Computer Systems and Networks, and (4) Innovative Computer Technology in Veterinary, Fishery and Agromaritime, Animal Science, Forestry, and Agricultural Engineering. The conference received 101 papers from 10 countries, out of which 49 papers have been accepted (acceptance rate of 48,51%). All papers have undergone a meticulous peer-review process based on their significance, novelty, and technical quality. Every paper was reviewed by at least three independent experts, with many experiencing even more reviews. The accepted papers will be proposed to be published in IEEE Xplore and indexed in Scopus.

Due to the Corona Virus 2019 Outbreak, we decided to deliver the conference virtually. The organizing committee had been working relentlessly to create a virtual conference that will be worthy and engaging for both presenters and attendees. The full conference format is a mix of pre-recorded and asynchronous engagement, and live engagement through discussion and in-person video calls.

I would like to especially thank the Organizing Committee, without the enthusiastic and hard work of a number of colleagues, the 1st ICOSICA 2020 would not have been realized. I wish to express my sincere appreciation to the Technical Program Committee members and the reviewers for their commitment and contribution. I would also like to thank the Keynote and Invited Speakers for contributing to this indispensable part of the program. We also thank the IEEE Indonesia Section for their great support and mentorship as technical sponsor of the conference. Last, but certainly not least, my thanks go to all the authors who had submitted papers and all the attendees. I hope that you will find the program is encouraging and a source of brilliant ideas for future research. I thank you for virtually attending the 1st ICOSICA 2020. I extend my warmest congratulations and wish the 1st ICOSICA 2020 to be a great success.

Irman Hermadi, PhD | General Chair of ICOSICA 2020

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ABOUT IPB UNIVERSITY

IPB has a long history of struggle. Started from the University of Indonesia in Bogor, the university became the forerunner of the first agricultural higher education in the country in 1940. Furthermore, based on the Decree of the Minister of Higher Education and Science No. 91/1963 and ratified by Presidential Decree of the Republic of Indonesia No. 279/1965 on 1 September 1963, IPB secede from its parent university to become the Agricultural Institute in Bogor.At this time IPB is the only college in Indonesia that bears the name of "institute" same level as university that given full trust by the government to pursue and develop tropical agricultural sciences in the country. Since 53 years ago, IPB has grown and developed into a college that has a good reputation and plays an important role in the national development and higher education in Indonesia. IPB’s growth and development has gone through three periods of time: the colonial era, the period of struggle for independence, and the period after the proclamation of independence. In the three periods there was a consistent thread that consists of the striving, nationality, patriotism and leadership of the predecessors. These are the values that have always been held firmly from time to time. The tradition of community service, which is an integral part of higher education’s Tridharma (three main responsibilities), was also born from this campus. The action research that began in 1963 in Karawang and in cooperation with various universities and other institutions has led the Indonesia nation to significantly increase rice production and even achieve self-sufficiency in rice. Meanwhile, IPB’s strength in the establishment of general competence turns out to have led the students to become graduates who are ready to play a role in many areas of life. The values that can be learned from a wide range of teaching and learning in IPB are familiarity of managing complexity and uncertainty, excellence in numerical and scalable solutions, always thinking of systems and caring for the environment, always thinking and acting systemically, complying to the regulations, and always caring for farmers as well as readily helping others. Now at the age of 53 years, IPB has nine faculties: the Faculty of Agriculture, Faculty of Veterinary Medicine, Faculty of Fisheries and Marine Sciences, Faculty of Animal Science, Faculty of Forestry, Faculty of Agriculture, Faculty of Mathematics and Natural

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Sciences, Faculty of Economics and Management, Faculty of Human Ecology, Business School, Graduate School and Vocational School. IPB currently has 36 departments, 21 Study Centers, 159 undergraduate and graduate programs and 18 diploma (vocational) educational programs. And until January 2016, IPB has graduated 133,778 students.

IPB University Main Campus, Bogor, Indonesia.

Contacts Jl. Raya Dramaga Kampus IPB Dramaga Bogor 16680 West Java, Indonesia +62 251 8622642 [email protected] ipb.ac.id

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ABOUT DEPARTMENT OF COMPUTER SCIENCE, IPB UNIVERSITY

The Computer Science Study Program in FMIPA IPB was opened in the 1993/1994 academic year. Then, on September 17, 1998 the Computer Science Study Program was established as the Department of Computer Science under the Faculty of Mathematics and Natural Sciences IPB through IPB Rector's Decree No. 095/K.13/HK/OT/1998. The mandate given to the Department of Computer Science is the development of computer science and its application in the field of information and communication technology (ICT). In September 2004, the office of the Department of Computer Science FMIPA IPB which was previously located on the IPB Baranangsiang Campus was moved to the IPB Darmaga Campus. Currently, the department consists of bachelor, master, and doctoral programs in computer science.

Contacts Departemen Ilmu Komputer Jl Meranti Wing 20 Level 5 Kampus IPB Darmaga 16680 +62 251-8625584 [email protected] cs.ipb.ac.id

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COMMITTEES

Steering Committee Prof. Dr. Ir. Agus Buono, M.Si., M.Kom, IPB University, Indonesia. Dr. Azwirman Gusrialdi, Tampere University of Technology, Finlandia.

Conference Committee General Chair Dr. Irman Hermadi, M.S., IPB University, Indonesia.

General Co-Chair Auzi Asfarian, S.Komp., M.Kom., IPB University, Indonesia.

Financial and Sponsorship Chair Dr. Karlisa Priandana, M.Eng., IPB University, Indonesia. Meuthia Rachmaniah, M.Sc., IPB University, Indonesia. Lailan S Hasibuan, M.Kom., IPB University, Indonesia. Wulandari, M.Eng., IPB University, Indonesia

Program Chair Dr. Sony H Wijaya, M.Kom., IPB University, Indonesia. Wulandari, M.Eng., IPB University, Indonesia.

Publication Chair Dr. Yani Nurhadryani, M.T., IPB University, Indonesia.

Publicity and Public Relationship Chair Firman Ardiansyah, M.Si., IPB University, Indonesia. Auriza R Akbar, M.Kom., IPB University, Indonesia.

Local Committee Dean A Ramadhan, M.Kom., IPB University, Indonesia. Irvan Y Ramdhan, IPB University, Indonesia.

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Technical Program Committee Technical Program Chairs Dr. Medria K Hardienata, IPB University, Indonesia.

TPC Member Dr. Mohd Noor Bin Derahman, Universiti Putra Malaysia, Malaysia. Dr. Yani Nurhadryani, IPB University, Indonesia Dr.Eng Wisnu Ananta Kusuma, IPB University, Indonesia. Dr. Hendra Rahmawan, IPB University, Indonesia. Dr. Karlisa Priandana, M.Eng., IPB University, Indonesia.

Reviewers A Afiahayati, Gadjah Mada University (UGM), Indonesia Abdullah bin Muhammed, Universiti Putra Malaysia, Malaysia Achmad Solichin, Budi Luhur University, Indonesia Adzkia Salima, Wageningen University, Netherlands Afia Hayati, Gadjah Mada University (UGM), Indonesia Agus Buono, IPB University, Indonesia Agustami Sitorus, Indonesian Institute of Sciences (LIPI), Indonesia Ahmad Ridha, IPB University, Indonesia Albert Yosua, Tokyo Institute of Technology, Japan Andrew Schauf, Nanyang Technological University, Singapore Annisa Annisa, IPB University, Indonesia Aries Fitriawan, University of Indonesia, Indonesia Arli Aditya Parikesit, Indonesia International Institute for Life Sciences (i3L), Indonesia Asem Kasem, Universiti Teknologi Brunei, Brunei Asif Zaman, University of Rajshahi , Bangladesh Auriza Rahmad Akbar, IPB University, Indonesia Auzi Asfarian, IPB University, Indonesia Ayu Purwarianti, Bandung Institute of Technology (ITB), Indonesia Bayu Erfianto, Telkom University, Indonesia Chastine Fatichah, Sepuluh Nopember Institute of Technology (ITS), Indonesia Dean Apriana Ramadhan, IPB University, Indonesia Deshinta Arrova Dewi, INTI International University, Malaysia Didik Utomo, Nagoya University, Japan

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Dilini Samarasinghe Widana Arachchige, University of New South Wales (ADFA), Australia Endah Ratna Arumi, Muhammadiyah University, Magelang, Indonesia Erandi Lakshika, University of New South Wales, Canberra (UNSW Canberra)., Australia Fahren Bukhari, IPB University, Indonesia Fatimah Abdul Razak, Universiti Kebangsaan Malaysia, Malaysia Ferry Astika Saputra, Electronic Engineering Polytechnic Institute of Surabaya (PENS), Indonesia Firman Ardiansyah, IPB University, Indonesia Firman Sasongko, Rolls-Royce@NTU, Singapore Gede Indrawan, Ganesha University of Education, Indonesia Hari Agung Adrianto, IPB University, United Kingdom Harry Budi Santoso, University of Indonesia, Indonesia Hendra Rahmawan, IPB University, Indonesia Heru Sukoco, IPB University, Indonesia Hjh Nor Zainah binti Hj Siau, Universiti Teknologi Brunei, Brunei Ibrahim Umar, Institute of Marine Research, Norway Imas Sukaesih Sitanggang, IPB University, Indonesia Ionia Veritawati, Pancasila University, Indonesia Irman Hermadi, IPB University, Indonesia Jaziar Radianti, University of Agder, Centre for Integrated Emergency Management, Norway Julio Adisantoso, IPB University, Indonesia Karlisa Priandana, IPB University, Indonesia Khaironi Yatim bin Sharif, Universiti Putra Malaysia, Malaysia Lailan Sahrina Hasibuan, IPB University, Indonesia Leon A. Abdillah, LAA, Bina Darma University, Indonesia Lukmanul Hakim Zaini, IPB University, Indonesia Mahardhika Pratama, Nanyang Technological University, Singapore Mahirah binti Jahari, Universiti Putra Malaysia, Malaysia Maria Susan Anggreainy, IPB University, Indonesia Marimin Marimin, IPB University, Indonesia Markus Santoso, University of Florida, USA Mayanda Mega Santoni, Universitas Pembangunan Nasional Veteran (UPNV) Jakarta, Indonesia

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Md. Altaf-Ul-Amin, Nara Institute of Science and Technology, Japan Medria Kusuma Dewi Hardhienata, IPB University, Indonesia Melinda Melinda, Syiah Kuala University, Indonesia Meuthia Rachmaniah, IPB University, Indonesia Mochamad Asri, Facebook, USA Mohammad Saiful bin Haji Omar, Universiti Teknologi Brunei, Brunei Mohd Helmy Abd Wahab, Universiti Tun Hussein Onn Malaysia, Malaysia Mohd Noor Bin Derahman, University Putra Malaysia, Malaysia Mohd Taufik bin Abdullah, Universiti Putra Malaysia, Malaysia Muhammad Asyhar Agmalaro, IPB University, Indonesia Muhammad Fadli Prathama, PLN Institute of Technology (formerly STT-PLN), Indonesia Muhammad Hasannudin Yusa, BIG Indonesia, Indonesia Muhammad Irwan Padli Nasution, State Islamic Univeristy, Sumatera Utara, Indonesia Muhammad Rasyid Aqmar, Rakunten Institute of Technology, Japan Muharfiza Muharfiza, Indonesian Polytechnic of Agricultural Engineering, Indonesia Muqtafi Akhmad, Tokyo Institute of Technology, Japan Mushthofa Mushthofa, IPB University, Indonesia Mutsawashe Gahadza, Econet Wireless Zimbabwe, Zimbabwe Noor Akhmad Setiawan, Gadjah Mada University (UGM), Indonesia Noor Cholis Basjaruddin, Bandung State Polytechnic, Indonesia Novanto Yudistira, Brawijaya University, Indonesia Nur Afny Catur Andryani, Tanri Abeng University, Indonesia Nur Hasanah, IPB University, Indonesia Nurulhuda Khairudin, Universiti Putra Malaysia, Malaysia Riko Arlando Saragih, Maranatha Christian University, Indonesia Rina Trisminingsih, IPB University, Indonesia Rudolf Jason, Tokyo Institute of Technology, Japan S H Shah Newaz, Universiti Teknologi Brunei, Brunei Sabita Maharjan, University of Oslo, Norway Setiawan Hadi, Padjadjaran University, Indonesia Setyanto Tri Wahyudi, IPB University, Indonesia Shadi Abpeikar, University of New South Wales (ADFA), Australia Shelvie Nidya Neyman, IPB university , Indonesia Shuo Yang, University of New South Wales (ADFA), Australia

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Sony Hartono Wijaya, IPB University, Indonesia Sri Wahjuni, IPB University, Indonesia Sritrusta Sukaridhoto, Electronic Engineering Polytechnic Institute of Surabaya (PENS), Indonesia Sunu Wibirama, Gadjah Mada University (UGM), Indonesia Supriyanto Supriyanto, IPB University, Indonesia Suyanto Suyanto, Telkom University, Indonesia Taufik Djatna, TDJ, IPB University, Indonesia Temmy Hendrawan, Bandung Institute of Technology (ITB), Indonesia Tenia Wahyuningrum, Telkom Institute of Technology, Purwokerto, Indonesia Teny Handhayani, University of York, USA Terje Gjosaeter, Oslomet University, Norway Teuku Muhammad Roffi, Pertamina University, Indonesia Toto Haryanto, IPB University, Indonesia Triwiyanto Triwiyanto, Health Polytechnic Ministry of Health Surabaya, Department of Electromedical Engineering, Indonesia Tutun Juhana, Bandung Institute of Technology (ITB), Indonesia Uky Yudatama, Muhammadiyah University, Magelang, Indonesia Vektor Dewanto, University of Queensland, Australia Wida Susanty Haji Suhaili, Universiti Teknologi Brunei, Brunei Widodo Widodo, Jakarta State University (UNJ), Indonesia Wisnu Ananta Kusuma, IPB University, Indonesia Wiwin Suwarningsih, Indonesian Institute of Sciences (LIPI), Indonesia Wulandari Wulandari, IPB University, Indonesia Yani Nurhadryani, IPB University, Indonesia Yeni Herdiyeni, IPB University, Indonesia Yus Sholva, Tanjungpura University, Indonesia

CONFERENCE SCHEDULE

All time in the program schedule is in Western Indonesia Time / Waktu Indonesia Barat (WIB; GMT+7). Please pay attention and adjust it to your local time. Current time in WIB can be found on http://time.bmkg.go.id.

Time Agenda

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(GMT +7)

07.30 – 08.15 Registration

08.15 – 08.45 Opening Remarks (@ 10 mins) Prof. Dr. Ir. Agus Buono, M.Si., M.Kom Chair, Dept. of Computer Science, IPB University

Dr. Ir. Sri Nurdiati, M. Sc Dean, Faculty of Mathematics and Natural Sciences, IPB University

Prof. Dr. Arif Satria SP, M.Si Rector, IPB University

08.45 – 09.15 Keynote Speech Dr. Fadjry Djufry (Head of Indonesian Agency for Agricultural Research and Development)

09.15 – 09.25 Break

09.25 - 10.05 Invited Speakers (30 mins presentation, 10 mins QA) Professor Dr. Rusli Bin Haji Abdullah, University Putra Malaysia, Malaysia

10.05 - 10.45 Invited Speakers (30 mins presentation, 10 mins QA) Prof. Naoshi Kondo, Kyoto University, Japan

10.45 - 11.25 Invited Speakers (30 mins presentation, 10 mins QA) Dr Wida Susanty binti Haji Suhaili, Universiti Teknologi Brunei, Brunei Darussalam

11.25 - 12.05 Invited Speakers (30 mins presentation, 10 mins QA) Dr Wisnu Ananta Kusuma, IPB University, Indonesia

12.05 - 13.00 Break

13.00 - 15.20 Parallel Session

15.20 - 16.00 Break

16.00 - 16.30 Closing

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INVITED SPEAKERS

Prof. Naoshi Kondo

Prof. Naoshi Kondo is currently a professor, Graduate School of Agriculture, Kyoto University and is working on automation and sensing systems in agriculture, livestock and aquaculture aiming precision farming. He graduated from undergraduate and graduate schools (Department of Agricultural Engineering), Kyoto University in 1982 and 1984 respectively, and was engaged at Okayama University in 1985 as an assistant professor for 15 years. He has received many academic awards for his works from many kinds of societies: JSAM, JSME, SHITA, ASAE, SAS, AABEA, JSABE, JAICABE, MAFF, JATAFF, AJASS. He was given the Japan Prize of Agricultural Science, which is one of the oldest and top awards in agricultural fields from AJASS and the Yomiuri Shimbun on April 5, 2017. His achievement was “Sensing System Based Bio-Production Intelligent Robots.”

Prof. Rusli Abdullah

Prof. Rusli Abdullah is currently Professor of Knowledge Management at Software Engineering and Information System Department, as well as the Dean of Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM). Specialized in Knowledge Management, he has completed his PhD in Computer Science at Universiti Teknologi Malaysia (UTM), Johor, Malaysia in 2006. Starting his career as a lecturer in 2006 after serving as System Analyst for 8 years at UPM. He has published over 14 books and over 100 journal papers, conference publications and other academic research publications. He has also received numerous awards and recognitions throughout his career that includes Silver Medal for University Research Invention in 2008 and 2009, Gold Medal for University Research Invention 2005, Silver Medal for Invention of Technology and Exposition I-TEX (2009), Silver Medal for Malaysian Research Invention and Exposition – MTE (2010, 2012 & 2014) and Certification of University Excellent Services of Lecturer Service for many years since 1999.

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Dr. Wida Susanty Haji Suhaili

Dr. Wida Susanty Haji Suhaili is the Assistant Professor at the School of Computing and Informatics, the Universiti Teknologi Brunei (UTB). She started her career in UTB on 2nd November 2004 upon completion of her Masters and later completed her PhD in 2015. Within the university, she is the project coordinator for the School of Computing and Informatics where she foresees all the students’ projects from the Computing group, Final year and Master research. She is currently the thrust’s lead for Digital and Creativity research thrust in the University.

Her works revolves around the concept of research, development and deployment where she puts research beyond development towards deployment in an actual setting. She has focused on SMART Initiative project involving Internet of Things and has started working on peatland projects since 2016. She has received support from various stakeholders from government agencies and industries and is an active member of Brunei Shell Joint Venture, Biodiversity Action Plan (BAP). She also focused on the adoption of Science, Technology and Innovation in the improvement of paddy plantation in Brunei which then continued as her fellowship engagement to represent Brunei in the ASEAN S&T Fellowship for 2019/2020. Her fellowship is in close collaboration with Department of Agriculture and Agrifood, Ministry of Primary Resources and Tourism, Brunei.

She is the national finalist representing Brunei in the ASEAN-US Science Prize for Women supported by UL and USAID 2020 under the theme of preventive healthcare with her research on the mitigation of forest fire in particular peatland through the use of Internet of Things. With her focus and committed involvement to the aforementioned projects, she has been actively invited as speaker and panelist for conference, forum and symposium locally and regionally where she has taken the opportunity to advocate the use of IoT in the advancement of technology in Brunei towards IR4.0.

Dr. Wisnu Ananta Kusuma

Dr Wisnu Ananta Kusuma is a prominent researcher in the Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University. He also acts as an executive secretary of the Tropical Biopharmaca Research Center, IPB University and works on research related to bioinformatics and herbal medicine. He is also pioneering two web-based applications for bioinformatics: ISNIP for Single Nucleotide Polymorphism Identification and IJAH Analytics a prediction system for the formula of herbal medicine (Jamu) based on machine learning and network pharmacology approach. Recently, he also participates as a part of the team who implement the machine learning technique for virtual screening on Indonesia herbal compounds as COVID-19 supportive therapy.

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TECHNICAL PARALLEL SESSION SCHEDULE

Parallel 1 Software Engineering and Information Science

Time Title and Authors

13.00-13.20 Design of Green ERP System Reverse Logistic Module Based on Odoo in Leather Tanning Industry Hennry Syahreza Arifin, Ari Yanuar Ridwan, Muhardi Saputra

13.20-13.40 Supply Chain Management Application System for Recording Chili Production and Distribution Meuthia Rachmaniah

13.40-14.00 Metaphor Design in Localising User Interface for Farmers in Malaysia Using User-Centered Approach Novia Indriaty Admodisastro

14.00-14.20 Preliminary User Studies on Consumer Perception Towards Blockchain-Based Livestock Traceability Platform in Indonesia: An Implication to Design Auzi Asfarian, Kautsar I Hilmi, Irman Hermadi

14.20-14.40 A Proof-of-Concept of Farmer-to-Consumer Food Traceability on Blockchain for Local Communities Jiranuwat Jaiyen, Suporn Pongnumkul, Pimwadee Chaovalit

14.40-15.00 Tokocabai Marketplace Application Based on Web Using Extreme Programming Method Meuthia Rachmaniah

15.00-15.20 Web Service-Based Automata Testing: A Case Study of Online Airline Reservation Amir Rizaan Abdul Rahiman, Temitope Betty Williams, Izuka Joseph Iheanocho

Parallel 2 Computational Intelligence and Optimization

Time Title and Authors

13.00-13.20 GPUs Utilization of Residual Network Training for Colon Histopathological Images Classification Toto Haryanto, Heru Suhartanto, Aniati M. Arymurthy, Kusmardi

13.20-13.40 Development of GPU-Accelerated Pre-Processing Chain for LAPAN-A2 Multispectral Imagery Kamirul Kamirul, Wahyudi Hasbi, A. Hadi Syafrudin

13.40-14.00 Implementation of Breadth-First Search Parallel to Predict Drug-Target Interaction in Plant-Disease Graph Alvin Reinaldo, Wisnu Ananta Kusuma, Hendra Rahmawan, Yeni Herdiyeni

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14.00-14.20 Dynamic Route Optimization for Waste Collection Using Genetic Algorithm Abdullah Alwabli, Ivica N. Kostanic

14.20-14.40 Application of Skyline Query on Route Selection (the Case Study of Bogor City Roadway) Lilis Gumilang Asri, Annisa Annisa

14.40-15.00 Recommendation System Based on Skyline Query: Current and Future Research Ruhul Amin, Taufik Djatna, Annisa Annisa, Imas Sukaesih Sitanggang

15.00-15.20 Association of Single Nucleotide Polymorphism and Phenotypes in Type 2 Diabetes Mellitus Using Genetic Algorithm and CatBoost Hilmi Farhan Ramadhani, Wisnu Ananta Kusuma, Lailan Hasibuan, Rudi Heryanto

Parallel 3 Computational Intelligence and Optimization

Time Title and Authors

13.00-13.20 Enhancing Crops Production Based on Environmental Status Using Machine Learning Techniques Shiyam Talukder, Habiba Jannat, Sukanta Saha, Katha Sengupta, Muhammad Iqbal Hossain

13.20-13.40 Comparison of Machine Learning Models for Rainfall Forecasting Nazli Mohd Khairudin, Norwati Mustapha, Teh N M Aris, Maslina Zolkepli

13.40-14.00 Implementation of Borda Clustering to Form Zakat Receiving Villages Clusters Based on Region Potency in Indonesia Dony Rahmad Agung Saputro, Annisa Annisa

14.00-14.20 A Design of Traceability System in Coffee Supply Chain Based on Hierarchical Cluster Analysis Approach I Gusti Made Teddy Pradana, Taufik Djatna

14.20-14.40 Supply Chain Sustainability Assessment System Based on Supervised Machine Learning Techniques: The Case for Sugarcane Agroindustry Sri Mursidah, Taufik Djatna, Marimin Marimin, Anas Fauzi

14.40-15.00 Artificial Neural Network for Estimation Nutrient Utilization Based on Chemical Composition on Ruminant Animal Feed Toto Haryanto, Dias Febrisahrozi, Anuraga Jayanegara, Aziz Kustiyo

15.00-15.20 Ensemble Learning for Predictive Maintenance on Wafer Stick Machine Using IoT Sensor Data Achmad Mujib, Taufik Djatna

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Parallel 4 Innovative Computer Technology in Veterinary, Fishery, Animal Science, Forestry, and Agricultural Engineering & Software Engineering and Information Systems

Time Title and Authors

13.00-13.20 IoT-Based Environmental Monitoring System for Brunei Peat Swamp Forest Syazwan Essa, Rafidah Petra, Mohammad Rakib Uddin, Nur Ikram Ilmi and Wida Haji Suhaili

13.20-13.40 VIRTUAL KIOSK: TAMAN HERBA Muhamad Nazmi Said, Norhalina Senan, Muhammad Fakri Othman, Mohd Helmy Abd Wahab, Mohd Derahman

13.40-14.00 Development of an Aircraft Type Portable Autonomous Drone for Agricultural Applications Kazi Mahmud Hasan, Wida Haji Suhaili, Md. Shamim Ahsan, S H Shah Newaz

14.00-14.20 Predicting Methane Emission from Paddy Fields with Limited Soil Data by Artificial Neural Networks Chusnul Arif, Budi Setiawan, Nur Hasanah

14.20-14.40 Experimental Evaluation of IoT Connectivity Using LoRaWAN for Vending Machine NetworkRatna Mayasari, Renaldi Permana Putra, Aditya Kurniawan, Farhan Fathir Lanang, Ibnu Alinursafa, Budi Syihabuddin

14.40-15.00 The Potential for Implementing a Big Data Analytic-Based Smart Village in Indonesia Eneng Tita Tosida, Yeni Herdiyeni, Marimin Marimin and Suprehatin Suprehatin

15.00-15.20 Drug-Target Visualization on IJAH Analytics Using Sankey Diagram Muhammad Fadhil Al-Haaq Ginoga, Rina Trisminingsih, Wisnu Ananta Kusuma

Parallel 5 Computational Intelligence and Optimization

Time Title and Authors

13.00-13.20 Image Processing for Diagnosis Rice Plant Diseases Using the Fuzzy System Anwar Rifai, Deni Mahdiana

13.20-13.40 Chilli Quality Classification Using Deep Learning Sudianto Sudianto, Anggra Haristu, Yeni Herdiyeni, Medria Hardhienata

13.40-14.00 CNN with Multi Stage Image Data Augmentation Methods for Indonesia Rare and Protected Orchids Classification Dimas Sony Dewantara, Rachmat Hidayat, Heru Susanto, Aniati M. Arymurthy

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14.00-14.20 Detection and Classification of Diseased Mangoes Akshay Koushik H, Ritanya B Bharadwaj, Ram Prasad E Naik, Ramesh G, Yogesh M J, Sana Habeeb

14.20-14.40 Estimating Crop Water Stress of Sugarcane in Indonesia Using Landsat 8 Restu Triadi, Yeni Herdiyeni, Suria Darma Tarigan

14.40-15.00 Development of Landslide Victim Detection System Using Thermal Imaging and Histogram of Oriented Gradients on E-PUCK2 Robot Wulandari Wulandari, Muhammad Arrazi, Karlisa Priandana

15.00-15.20 Sentinel-1A Image Classification for Identification of Garlic Plants Using a Decision Tree Algorithm Risa Intan Komaraasih, Imas Sukaesih Sitanggang, Muhammad Agmalaro

Parallel 6 Computer System and Networks & Software Engineering and Information Systems

Time Title and Authors

13.00-13.20 High Availability Bidirectional Forwarding Detection (BFD): Fast Recovery Mechanism Provide Multi-Circuit Service Provider Hillman Akhyar Damanik

13.20-13.40 Study and Experiment of Generalized Frequency Division Multiplexing Implementation Using USRP and LabVIEW Ajib S. Arifin

13.40-14.00 Development of Autonomous UAV Quadcopters Using Pixhawk Controller and Its Flight Data Acquisition Karlisa Priandana, Muhammad Hazim, Wulandari, Benyamin Kusumoputro

14.00-14.20 An Initial Framework of Dynamic Software Product Line Engineering for Adaptive Service Robot I Made Murwantara

14.20-14.40 Smart Governance for One-Stop-Shop Services of Bio-Business Licensing in Indonesia: a Literature Review Muhammad Mahreza Maulana, Arif Suroso, Yani Nurhadryani, Kudang Seminar

14.40-15.00 Design of Smart System for Fruit Packinghouse Management in Supply Chain Ali Khumaidi, Yohanes Purwanto, Heru Sukoco, Sony Hartono Wijaya, Risanto Darmawan

15.00-15.20 Development of Back-End of a Rural Participation Based Knowledge Management System of Smallholder Palm Plantation Irman Hermadi, Andi Muhammad Chaerul Hafidz, Auzi Asfarian, Yani Nurhadryani, Nadya Farchana Fidaroina

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Parallel 7 Computational Intelligence and Optimization

Time Title and Authors

13.00-13.20 Intelligent Spatial Decision Support System Concept in the Potato Agro- Industry Supply Chain Rindra Yusianto

13.20-13.40 Multi-UAV Coordination for Crop Field Surveillance and Fertilization Leonardi Fabianto, Karlisa Priandana, Medria Hardhienata

13.40-14.00 Implementation of Recursive Least Square for Basic Piano Chords Noise Reduction Bobby Jonathan, Agus Buono

14.00-14.20 Application of Ant Colony Optimization for the Selection of Multi-UAV Coalition in Agriculture Vito M. M. O. Ompusunggu, Medria Hardhienata, Karlisa Priandana

14.20-14.40 Temporal Prediction Model for CO and CO2 Pollutants Using Long Short Term Memory Muhammad Iqbal Shiddiq, Imas Sukaesih Sitanggang, Muhammad Agmalaro

14.40-15.00 Application of Gravitational Search Algorithm on the Modified Compartmental Absorption and Transit Model for Predicting Oral Drug Absorption Nurfaizah Nurfaizah, Agus Kartono, Tony Sumaryada

15.00-15.20 Prediction of Therapeutic Usage of Jamu Based on the Composition of Metabolites Using Convolutional Neural Network Sony Hartono Wijaya, Wafa Azyati, Lailan Hasibuan, Dean Apriana Ramadhan

Advanced Book | ICOSICA 2020, IPB University, Indonesia, 17 September 2020 | 24 2020 International Conference on Computer Science and Its Application in Agriculture (ICOSICA) The Potential for Implementing a Big Data Analytic-based Smart Village in Indonesia

Eneng Tita Tosida Yeni Herdiyeni Computer Science Dept. Computer Science Dept. IPB University IPB University Bogor, Indonesia Bogor, Indonesia [email protected] [email protected]

Suprehatin Suprehatin Marimin Agrobusiness Dept. Industrial Technology Dept. IPB University IPB University Bogor, Indonesia Bogor, Indonesia [email protected] [email protected]

Abstract— Smart village is one of the solutions to reduce poverty Government policy support is certainly not capable of in rural areas. The main objective of this research is to map the increasing development in villages and reducing the disparity potential implementation of the concept of smart villages based between rural and urban areas. One of the reasons is there on big data analytics in Indonesia. This research was conducted remains a significant gap in allocating resources. The resource through the elaboration of text mining-based Systematic gap in question includes human resources and infrastructure, Literature Review (SLR) with multiple regression analysis of which are still very low in rural areas when compared to those the 2018 Village Potential Data in Indonesia. The contribution resources allocated to the city. This condition can be described of this study is the production of a map describing the potential comprehensively through Indonesian Village Potential Data. for implementing smart villages based on big data analytics in One of the methods that have been investigated by researchers Indonesia. SLR cluster analysis produces a dendrogram that to reduce the disparity between villages and cities is by the maps the basic terminology of smart villages based on big data analytic. Indonesia has quite substantial economic and social implementation of smart villages [1][2]. capital resources, which has a positive effect on the poor and The concept of smart villages implemented in several 2 farmers/fishermen (R = 0.9759 and 0.9482) in the villages. This countries is based on the smart city development concept. This occurs through a mix of regional budget revenue (APBD) and concept certainly needs to be adjusted to the conditions and local self-subsistent (Swadaya) funding schemes in the potential factors present in these villages. Essential factors that management of agricultural and non-agricultural small support the success of smart villages in various countries businesses in the village. Indonesia also has sufficient capital for include: resources, technology, infrastructure, and four-parties managing information and communication technology (ICT) in the village for the development of big data analytic smart synergy (academic, business, community, government). Four- villages. There is a relatively strong influence on the poor and parties synergies are crucial to designing, building, farmers/fishermen (R2 = 0.5946 and 0.6006). Therefore, the implementing, controlling, and maintaining the sustainability challenge for future research to develop a smart village model of smart village programs [1][2][3]. The development for based on big data analytics that is appropriate to the territory implementing smart villages is increasing, along with the of Indonesia. This model needs to be elaborated with diverse rapid growth of information and communication technology factors including economic, social, cultural and smart (ICT). ICT factor that is crucial to the success of smart villages educational potential as well should include indicators of the in several countries. ICT support is regulated by Permendesa potential for data technology available on various media, No. 6 of 2020, because it outlines the strategic areas, to through the framework of agriculture big data analytic. prioritise for rural development in Indonesia. Especially in the Keywords— Big data analytic, Clustering, Multiple regression era of data disruption, the readiness of ICTs systems provides Smart village, Systematic Literature Review greater opportunities for the development of smart villages based on big data analytics [4][5][6]. Research shows I. INTRODUCTION implementing big data analytics has been successful in Indonesia is an archipelago and is strengthened by rural climate-smart agriculture (CSA) based village programs areas in which a percentage of agricultural land use more than [7][8]. CSA programs that have been adopted in the smart urban areas. In 2018 the Central Bureau of Statistics (BPS) village models are mostly used for on-farm processes. reported that Indonesia had 74,517 villages. In addition, there Research on the application of big data analytics in the smart are 919 Nagari in West Sumatra, 8,444 subdistricts, and 51 village for the post-harvest process and analysis of the Transmigration Settlement Units (UPT) in Indonesia. potential of the smart village has not done much. In contrast, Development disparities in cities and villages still occur in the factors of resources, infrastructure, and technology that are Indonesia. This occurred due to factors influencing, the presented in Indonesian villages can mostly be portrayed increasingly massive urbanization process. In part, due to the through Village Potential Data published by BPS [9]. higher attractiveness of the city compared to the monotonous village. The disparity in village development in Indonesia was Implementation of big data, data analytic, data mining, addressed in legislation with the issuing of the Village Law sensors, virtual reality, augmented reality, 5G technologies No. 6 of 2014. The law places a priority on development are a need for future smart village research that is starting from in the village and the periphery. This law was collaborated as an ICT enrichment strategy [5][6]. This reinforced by the issuance of Permendesa No. 6 2020, which strategy needs to be synergized with a policy framework gave the Village Budget (APBDesa) the power to prioritize according to the conditions of local village wisdom. developments in the village. Therefore, the main challenge for future smart village

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research is how to increase the ICT literacy of the villagers, so that the smart village program is optimal. The strategies to increase the ICT literacy of villagers is closely related to citizen science [10][11]. The core of big data analytic that will be implemented in the smart village program must require at least three basic principles of big data (volume, velocity dan variety) [12][13][14]. The big data-based smart village has also been successfully carried out through the provision of electricity Fig. 1 Stages of research and clean water, which is equipped with an IoT-based control system [11][15][16]. This program is able to increase the The instrumentation of this research limited by using the productivity of villagers. This productivity includes secondary data. The data used is the 2018 Village Potential agricultural, entrepreneurial, education, and health activities. Data that is produced by BPS. This data consists of potential The implementation of smart transportation in the village area variables in village development that exist around of the has been done by using big data analytic concept [17]. Smart administratively village area. Some of these variables are: transportation has collaborated with smart tourism, which is ICT, management of program, sources of funds for the claimed as the smart village innovation in Liaoning Province. programs, agriculture and small-scale non-agricultural Implementation of a big data-based smart village must be businesses and beneficiary villagers. The selection of these adjusted to the basic needs of the villagers. The development variable refers to [1][2][20]. The selected variable then of the big data-based smart village in Indonesia has a wide manage by multiple regression technique to map the potential variety of opportunities. This is influenced by the condition of big data analytic-based smart village in Indonesia. of infrastructure, policies, and institutions of the economic, social, cultural, and also political value in each village. These III. RESULT AND DISCUSSION conditions can be represented by secondary data through A. Descriptive analysis of hierarchical attributes and Village Potential Data Indonesia. domains of smart village and citizen science research Therefore, the main objective of this research is to analyze The global condition of smart village research is the potential of implementing big data analytics-based smart illustrated using an analysis of attribute hierarchies. We villages in Indonesia. The analysis of the smart village's conducted our analyses using NVivo 12 Plus tool, and the potential was carried out using the stages of Systematic results are shown in Fig 2. The analysis of a hierarchy among Literature Review (SLR) and integrated to multiple the attributes of smart village research are arranged by year; regression. In this paper, we report an analysis of smart areas of Sustainable Development Goals (SDGs), followed villages based on big data analytic and the 2018 Village by research methods and technology. The process of our Potential Data in Indonesia. Our smart village SLR's is research on the hierarchy in the smart village domain that integrated with descriptive statistical analysis and clustering integrates with citizen science research is shown in Fig. 3. of smart village research and citizen science. Using The SLR result that represented in Fig 2 and Fig 3 refer to descriptive multiple regression analysis, we report the [18], which is managing the paper of smart village and citizen significant findings from the SLR about smart villages based science research by descriptive analysis through hierarchical on 2018 Village Potential Data. attributes and domain research.

II. METHOD Based on our findings, it can be concluded that the smart This research was carried out according to the stages of village research agenda in the future will have a potential to the SLR and multiple regression method, as shown in Fig. 1. be developed towards the construction, implementation and The SLR process was carried out with the NVivo 12 Plus comprehensive analysis of big data analytic technology. This tools and this process also refer to [18]. The use of 44 papers statement is supported by the availability of data and related to the smart village and 33 papers related to citizen technology that are becoming cheaper. Another support is science also refer to [18]. We analyzed the hierarchy of related to increasing access to various ICTs in rural areas, attributes and a hierarchy of the research domain [19] related demonstrated by an increase in ICT literacy in these areas. to the smart village and citizen science. One example of the uptake of ICT in Indonesia is the increasing use of social media for sale and purchase The research attributes hierarchy that is related to the transactions in rural areas that is higher than in cities. The rate smart village and citizen science will produce dendogram of usage of social media to sell online in the village 69.9% structure. This dendogram will map the density of smart compared to the city, 62.3%. Using social media to buy village research that is integrated into citizen science online in villages is 57.2%, while in cities 42.1% [21]. Even research. The map constructed by hierarchical form included internet usage by farmers in Indonesia has reached 10% of year publication, Sustainable Development Goals (SDGs) total farmers in Indonesia [22]. area, method and technology. This hierarchy will produce the basic terminology of smart village integrated citizen science This condition has a real potential opportunity for the as a foundation of the next model research. This basic development of smart villages based on big data analytics. terminology will be used as reference to determine the Socialization through social media and the promotion of variable selected from the 2018 Village Potential Data of smart villages has also become a research agenda for Indonesia. SLR results are discussed with big data analytic progress, so that many parties can reproduce the success of framework components, followed by further discussion, smart villages. There is research that proposes this will be about our analyses of 2018 Village Potential Data using able to narrow urban and rural disparities, but with synergistic multiple regression techniques. and sustainable four-parties support [1][2][3][5][20][23] [24][25][26].

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Fig. 2. Attributes Hierarchical Analysis of Smart Village Researches

Fig. 3. Attributes and Domain Hierarchical Analysis of Smart Village Researches

Fig 3 shows that the potential for developing smart village cluster analysis of citizen science research are shown in Fig research based on big data analytic is still very potential. The 4. The results of cluster analysis of the integration of smart results of the hierarchy analysis of the smart village research village research in citizen science are shown in Fig 5. domain and citizen science illustrate an area of research that is still narrow when compared to other research areas. Smart Cluster analysis through word frequency techniques were also carried out on 33 citizen science research papers to village research is still dominated by reviews related to social science (citizen science & crowdsourcing) and infrastructure produce results as five dominant words at the first level of the dendrogram, as shown in Fig 4. The focus of citizen science (ICT and non-ICT facilities, and policies). There are relatively less comments related to institutions in smart research is very closely related to smart village research.This is evidenced by the dominant use of the words project and village research, compared to other areas of research. We propose, this shows that the development of smart village data which are also dominant in the smart village research dendogram. The word project belongs to the same family as research using big data analytics and integration with social science, computer science and infrastructure; has significant a citizen. In this case, it shows that the reported citizen project activities [27][28]. The word data is closely related to potential to be carried out especially in relation to institutional collaboration. quality, its finding shows that citizen science research needs high quality data. Especially, for the formation of citizens as B. Cluster analysis of smart village and citizen science a scientist [29][30], this requires ICT and quality data research optimization techniques, to produce citizen science projects that have high validity [29][31][32][33]. What is very Smart village research cannot be separated from citizen interesting about this word frequency technique is the science research. Based on research finding [1][26] more than emergence of the farmer as a dominant word in the process 70% of smart village research is comprehensively discussed of citizen science research because it is implemented on through the citizen science approach. Therefore, in this study, farmer residents, most of whom live in villages [34][35]. cluster analysis is done through the investigation using the word frequency technique of various text mining approaches There are exciting findings from the results of our cluster in citizen science research, and the integration of smart analysis using word frequency techniques in the integration village research with citizen science research. The results of of smart village research and citizen science, with the results

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of the dendrogram structure, shown in Figure 5. Terminology As described, this condition is strengthened by the dominating the first level are the words smart and village. findings presented in the dendrogram of the integration of Word frequency techniques have good potential and ability smart village research and citizen science with a particular for SLR, particularly in this application to smart village focus on the terms : smart and village (first level); citizen and research. projects (second level); and model (third level). This case illustrates that most of the critical factors for the success of smart villages are part of the building blocks of citizen science. Other research reports, the development of citizen science is also very responsive to the development of ICT [8][36][37][38][39][40]. In Fig 5, at the sixth level is crowdsourcing, which is a branch shared with the words community and model. This interpreted to mean crowdsourcing an essential factor for the success of smart villages implemented through a citizen science project. The level of knowledge related to the role of its citizens can be divided into four levels : 1) crowdsourcing, citizens acting as sensors; 2) distributed intelligence, citizens as interpreter bases; 3) participatory science, citizens participating in the process of problem definition and data collection, 4) extreme or collaborative science, citizens play a role in the process of problem definition, data collection and data analysis [41][42]. Therefore, those who will be involved in a citizen science project can include professional scientists, credentialed scientists, academic scientists, residents, hobbyists, community members, volunteers, native villagers, and human sensors [42][43][44]. C. Integration of smart village domain SLR and big data management framework Hierarchical analysis of the attributes and domains of smart villages shows that the area of research related to big Fig. 4. The Dendrogram structure of citizen science's research data analytics being implemented and analyzed through the integration of citizen science and infrastructure remains a narrow field of research. This condition means there are

potential opportunities for development using this research method. Moreover, it is strengthened by the results of a descriptive analysis of the level ICT implemented. In particular, the integration of big data analytics for the Opinion Mining sub-sector, which is still minimal [5]. However, due to the era of data disruption, data optimization using big data

analytic technology has been widely used in various fields and is adopted in rural areas. The available data’s potential is abundant, being accessible through social media, online news, various government and private institutions' websites and other on-line sources. The data in question are related to

agricultural commodity price, agricultural market potential and other data that can be optimized using big data analytics.

The principle of big data management is built from a strong data foundation and develops according to the procedure in Fig 6. The big data management concept which is an integrates concepts described in [4]. Entities in smart villages that involve four-parties : 1) academics, 2) villagers

(community, cooperatives, micro small medium enterprises Fig. 5. Dendrogram structure of integration of smart village and citizen (MSMEs), and farmers), 3) government (ranging from science research villages level to nasional) and 4) businesses (entities from upstream to downstream). These entities can be optimized for developing smart management framework, that implemented in big data village big data management. An example of strong support analytics by [12] is shown in Fig 7. This case can be used to for the development of smart villages in Indonesia is Law identify and analyzed the components involved and influence No. 6 of 2014. This legislates that villages can be used as a the implementation of the big data concept in smart villages. basis for descriptive and predictive data to develop the The framework (Fig 7) shows that business processes are concept of big data analytics. The integration of the focused on the use of big data in the management of conceptual framework indicated in Fig 6 with agriculture agricultural processes.

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and the development of MSMEs; the involvement of villagers in ICT management activities. The ICT infrastructure and MSME potential studied in this study are limited to the results from source data the 2018 Village Potential Data elaboration in Indonesia [9]. Readiness to apply the concept of big data analytics for smart villages can be mapped through the conditions of basic needs, such as, the existence of a computer or laptop in the village. The presence of computers in the village office has a

significant influence on the readiness of the village to become Fig. 6. Conceptual of big data management a smart village. According to the availability of computers in the village as an indication of readiness, there are only two provinces with villages that are not equipped with computers (80% of village offices have no computers). The provinces are located in West Papua and Papua. More than 60% of village offices in 32 other provinces have been equipped with computers. This reflects on the outcome from Indonesia's initial capital to develop smart villages; initial capital to implement big data analytics [26]; and to focus strengthening smart government [2][45][46].

Internet functionality for the development of big data analytics has a vital role. The readiness of villages in Indonesia shows very diverse conditions. Villages that have more than 50% internet functionality are presence in the provinces of Central Java, DI Yogyakarta, East Java and Bali. This condition is following the accelerated development of

smart villages that have been initiated by DI Yogyakarta [2]. Fig. 7. The framework of a big data application system Villages in the provinces of West Java, Banten, West Nusa There are three parts in business processes: 1) data chain, Tenggara, Gorontalo and West Sumatra have internet 2) agricultural management, and 3) agricultural processes. functionality ranging from 20-30%. Even though West Java Through various decisions, data chains interact with already has a Jabar Cyber Province (JCV) program that began agricultural processes and agricultural management in 2013 and many ICT activities that collaboration in village processes. The stakeholder network includes all stakeholders [47] . The conditions for village internet development are still involved with this process, not only significant data users but low. The conditions for internet functionality in West Java are other actors such as the community, MSME cooperatives, and as follows: in 30% of functioning internet villages, 30% in the government. Network management is the organizational other villages internet conditions are rarely functioning, as structure and technology of the network that facilitates well as 30% of other villages the condition of the internet is coordination and management of processes carried out by not functioning even the rest has no internet. Villages with no stakeholder in the network management. The technology internet available still dominate the provinces of West Papua component focuses on an infrastructure of information that and Papua, and internet functionality in villages in other supports the data chain, while the organizational component provinces is still below 20%. This condition can be a focuses on governance and business models. reference for mapping the development of smart villages based on big data analytics [2] [5][48]. Data chain refers to the sequence data capture decision making and marketing of data [13][14]. The data chain Internet functionality is certainly strongly supported by includes all activities needed to manage data for agricultural the condition of internet signals that have entered the village. management. Fig 6 illustrates the main steps in this chain that Six provinces have 4G internet signals spread over more than are combined with the four components in the big data 40% of their villages, namely the Bangka Belitung Islands, framework. The big data analytic levels are descriptive, West Java, Central Java, DI Yogyakarta, Banten and Bali. diagnostic, predictive and prescriptive. The distribution of 3G internet signals with an average distribution in more than 30% of the villages has covered 20 The ideal concept in the big data analytic framework is provinces in Indonesia. The authorized capital of 3G and 4G very potential to be implemented in the smart village. This signals can be optimized for the application of big data smart village concept integrates components of social analytics related to the development of smart villages in sciences, computer science and infrastructure. The Indonesia. Although currently, 5G technology has become a establishment of the concept of smart villages based on big smart village research agenda [5], the availability of 3G and data analytics can be reviewed more comprehensively 4G technology can still be optimized with four-partiet through a portrait of the potential of villages in Indonesia. integration [45][49]. D. Descriptive analysis of Smart Villages Based on Big Data The most critical ICT infrastructure related to the Analytic Potencies in Indonesia availability of computers and the internet will not reach their potential to develop smart villages based on big data In this study, the potential of villages in Indonesia is analytics, if not supported by useful ICT management. portrayed according to : readiness of their infrastructure Potential villages in Indonesia show that there are nine directly related to ICT; sources of funding for ICT activities provinces where the percentage of villages is more than 50%

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has been managing ICT. The provinces are West Sumatra, apply this scheme to ICT management in their villages. This Riau, Bangka Belitung Islands, West Java, Central Java, DI finding reinforces understanding that agriculture and non- Yogyakarta, East Java, Bali and West Nusa Tenggara. This agricultural small businesses are supported by good ICT condition is critical because the contribution of ICT management in the village [53]. This situation becomes very management is an essential requirement in the development important for the basic capital in the application of smart of smart villages based on big data analytics [2][50][51][52]. villages based on big data analytics. The availability of infrastructure and management of ICTs The involvement of villagers in ICT management as the basis for implementing the concept of big data analytics activities is a form of social capital that needs to be in smart villages in Indonesia also needs to be supported by strengthened. This potential possessed by Indonesians, is robust funding. Funding sources for ICT management in confirmed by high rates of participation among citizens, poor villages have been strengthened by various schemes. The and farmers to contribute to various activities of ICT 2018 Village Potential Data in Indonesia confirms there are management. Likewise, the beneficiaries of ICT management 18 provinces that implemented Swadaya funding schemes for have been absorbed by various elements of villagers, ICT management, in the range from 5-25%. This independent including farmers and poor people spread across 22 provinces funding source shows the principle of independence and very with a percentage in the range 5-96%. This condition is in line high public awareness of the development of smart villages, with the primary objective of the concept of smart villages especially in the face of an era of data disruption and the that are focused on reducing poverty levels in the village. application of the concept of big data analytics [1][2]. This condition can be used as a powerful initial capital and is The potential application of a big data analytic smart village is also assessed from the relationship between the following the principle of smart village sustainability. Provinces of Yogyakarta, Bali, Aceh, Central Java, Central source of funds for managing agricultural businesses and non- agricultural small businesses in the village and the program Sulawesi and West Java have implemented more than 10% Swadaya schemes. The Yogyakarta Province shows the full beneficiaries. The study was conducted using multiple regression analysis, and the results are shown in Table 1. potential and readiness for the application of the concept of smart villages based on big data analytics compared with TABLE I. MULTIPLE REGRESSION ANALYSIS OF AGRICULTURE other provinces. In Yogyakarta, there has been a successful & NON-AGRICULTURE SME'S MANAGEMENT AND FUND implementation of smart villages in four village areas [2]. SOURCES TO BENEFICIARIES IN THE VILLAGE* APBD PAD Swadaya Others Community independence and government support through funding are also vital assets for the sustainability of Poor citizen big data analytic smart villages. Indonesia has the potential to Adjusted R Square 0.7040 implement smart villages based on big data analytics, because 27 provinces have already implemented a mixed scheme of Anova Signific. F 3.8800E-7 Regional Budget Revenue (APBD) and Swadaya, in the range Anova P-value 0.0004 0.0106 0.0429 0.0692 25-80%. The sustainability of smart villages based on big data Farmer / Fisherman analytics in Indonesia can be strengthened by the increasing number of provinces implementing funding sourced from Adjusted R Square 0.9759 Village Original Revenue (PAD), although the range 2-12% Anova Signific. F 7.7317E-24 is still low. The ten provinces : Jambi, Bengkulu, Bangka Anova P-value 0.00002 0.0044 0.2291 0.5058 Belitung Islands, Central Java, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Sulawesi, West Community business group Sulawesi and Maluku manage village ICT through a PAD Adjusted R Square 0.9482 scheme of more than 10%. This situation is interpreted as powerful capital and effort from the village manager in order Anova Signific. F 5.1609E-19 to face the challenges in the era of data disruption. This Anova P-value 0.0126 0.0024 0.0107 0.0023 situation can be used as the foundation of guaranteeing the Private / Businessman sustainability of the smart village concept based on big data analytics. Adjusted R Square 0.8567 Government and community synergy are a very effective Anova Signific. F 1.2138E-12 strategy to ensure the sustainability of the concept of smart Anova P-value 0.4171 0.3142 0.0428 0.8539 villages based on big data analytics. All provinces in *Source data processed from the 2018 Village Potential Data Indonesia have adopted this strategy to strengthen Table 1 shows our results from the villages analysed in management of ICT in villages. This strategy is demonstrated financial support from various schemes, and opportunities of through the implementation (8-58%) of APBD funding development smart villages based on big data analytics. This scheme and the mixed APBD-PAD scheme in all provinces. is indicated by the high value of Adjusted R Square. Even the This situation also occurs in the development of productive beneficiaries of the group of farmers/fishermen have a agricultural businesses and non-agricultural small businesses. powerful influence compared to other beneficiaries in All provinces in Indonesia have implemented this type of villages in Indonesia. This situation is relevant to the development by relying on mixed sources of regional objectives of the smart village program, which is more Swadaya funds in the range 30-95%. When combined data focused on reducing poverty levels of farmers/fishermen sources of ICT management funds and the development of [7][53]. The effect of funding sources of this program has a productive agricultural businesses and non-agricultural small differing when examined in detail using ANOVA analysis. businesses, shows high relevance among provinces that apply Other sources of funds for the management of agricultural Swadaya funding sources to business development; who also businesses and rural non-agricultural small businesses have

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no significant effect on the poor. This condition is different Indonesia already has a strong enough potential to from the sources of APBD, PAD and Swadaya funds which develop a smart village based on big data, when it is related have a significant effect on the poor. This finding has to the development of telematics SMEs which are scattered in excellent potential for the development of smart villages a small part of rural areas [54] [55]. The development of SME based on big data analytics because it demonstrates support telematics is closely related to the industrial revolution of the provincial and city/district governments is very high for technology 4.0 [56]. This has led to the rapid development of business development in the villages. smart cities as a potential for strengthening the big data-based smart village concept. The concept of smart village is also This scheme can be done through optimizing the sources increasingly sticking out along with various social, cultural, of APBD, PAD and Swadaya funds to reduce poverty for economic and political changes due to the Covid-19 villagers. Farmers/fishermen groups are significantly affected pandemic. Most of the community activities are carried out by this program by the Regional Budget and PAD funds. In through on-line facilities. Internet signal is a basic contrast, the power of community Swadaya influences the requirement for many activities, especially educational sustainability of smart village programs based on big data activities. The challenges of distance learning using multiple analytics. All of the program's funding sources have an effect e-learning platforms is becoming a very strong issue due to on community business groups. The source of Swadaya only the pandemic. This condition also occurs in rural areas, so it influences the private sector/entrepreneurs and this is becomes an opportunity to build smart education in a smart interpreted here as meaning, the private sector/entrepreneurs village ecosystem based on big data analytic. in the village have a right level of independence. The potential application of smart villages based on big data analytics was Education as one of the main goals in SDGs and smart examined using multiple regression and ANOVA analysis of villages [6], is also an area of focus for strengthening the ICT management and the variety of sources of funds for Indonesian government during a pandemic. This is done to beneficiaries from four groups of villagers (Table 2). ensure the continuity of education remains of high quality TABLE II. MULTIPLE REGRESSION ANALYSIS OF ICT even though it uses various online media. The adaptation MANAGEMENT AND FUND SOURCES TO BENEFICIARIES IN THE process continues to be carried out by the government and VILLAGE* even the community's role is getting stronger to support the APBD PAD Swadaya Others implementation of on-line based education [57]. This condition is a very strong challenges because rural areas in Poor citizen Indonesia that are connected to the internet are still relatively Adjusted R Square 0.5946 low. This condition encourages the government to further Anova Signific. F 3.2672E-06 strengthen internet infrastructure through the Palapa Ring. Palapa Ring will increase internet speed, especially in areas Anova P-value 0.1037 0.6620 0.7965 0.6119 that are difficult to reach. This effort is strengthened through Farmer / Fisherman the collaboration of the Open Transportation Network Program with the private sector. Adjusted R Square 0.6006 Anova Signific. F 2.6581E-06 This infrastructure is not only related to ICT infrastructure, but also includes institutions and regulations Anova P-value 0.0908 0.3623 0.7064 0.8509 [58]. This condition provides better opportunities for creating Community business group smart education, towards a smart community, smart people Adjusted R Square 0.9642 and smart society [52]. Referring to [6], the indicators of the success of smart education, especially those adapted for smart Anova Signific. F 2.50307E-21 village conditions, include three conversion factors : 1) smart, Anova P-value 0.0006 0.5690 0.4064 0.5442 2) sustain, 3) ICT. These three factors are related to six indicators : 1) percentage of primary school availability, 2) Most of citizen access to primary schools for a maximum of 30 minutes (if Adjusted R Square 0.9965 walking), 3) number of e-book titles in the school library, 4) Anova Signific. F 4.8495E-36 number of computers, laptops, tablets and other interactive digital media for learning media per class in basic schools, 5) Anova P-value 0.0000 0,.000 0.4821 0.4407 the number of computers, laptops, tablets and other *Source data processed from the 2018 Village Potential Data interactive digital media for learning media per class in Sources of funds for ICT management for beneficiaries of secondary schools, 6) the number of certified educational the poor and farmers/fishermen are not closely related in this activities in the fields of Science, Technology, Engineering program. In contrast, community business groups and citizens and Mathematics (STEM) per 10 thousand villagers. The in general who are strictly related to the ICT management challenge of achieving smart education indicators is indeed program and the source of funds used. This case shows that it very strong if it is mapped to the villages potential conditions is necessary to strengthen the four-party collaboration in the in Indonesia. However, this is an opportunity for future ICT management program in the village. With the program, research to develop various concepts, models and we can provide significant value to the effort to improve implementations of smart education in the big data-based living standards of the poor and farmers/fishermen. The smart village ecosystem. program will collaborate with various institutions including academics, government and private sector (industry, e- IV. CONCLUSION commerce, financial institutions, media) to provide various Efforts to reduce poverty in rural areas in several countries forms of encouragement to villagers to become more have been carried out through the implementation of smart independent and take the initiative when developing smart villages. The primary purpose of this study is to analyze the villages based on big data analytics. potential of implementing smart villages based on big data

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analytics in Indonesia. This research contributes to the The Swadaya source, which is collaborating with this analysis process that began with text mining-based SLR using other scheme, shows the principle of independence and very NVivo 12 Plus, and shows that the smart village research gap high public awareness for the development of smart villages, based on big data analytic has potential to be studied. SLR especially in the face of an era of data disruption and the results through cluster analysis also show the dendrogram of application of the concept of big data analytics. There is an essential factors that can be elaborated on the formation of exciting finding that reports provinces which apply smart village concepts based on big data analytics. The five independent sources of funds in business development also main factors are citizen, project, information, farmer and apply this scheme to ICT management in their villages. Our model. analysis reinforces the interpretation that agriculture and non- agricultural small businesses are indeed supported by proper The results of the SLR integration of the smart village ICT management in the village. This situation is very research domain and the big data analytical management important for social, economic and cultural capital applied to framework show the collaboration of four-party entities are the development of smart villages based on big data analytics. the main focus of the process driver in the big village analytic- based smart village concept. This four-parties optimization is This concept has the potential to be implemented, then strengthened by the quality of the data to be processed especially with the social, economic, cultural and political into information, knowledge and wisdom using the four stages changes due to the Covid-19 pandemic. The concept of a of big data management : 1) description, 2) diagnosis, 3) smart village based on big data analytic can be a new force to prediction and 4) prescription. For big data management to be build a smart community, through smart education, towards a able to add value to the villagers, the four-party collaboration smart society in a smart village ecosystem based on big data needs to be the focus of the next study. analytics. The strength of social, economic, political and cultural capital in smart village infrastructure based on big The results of the smart village research domain and the data is an important factor for the successful collaboration of big data management framework integrate with the infrastructure facilities (both ICT and non-ICT), institutions Indonesian Village Potential Data using multiple regression and regulations. Therefore, the challenges for future research techniques to review the potential application of big data is to develop smart village models based on big data analytics analytic smart villages in Indonesia. The agriculture and non- that are suitable for Indonesia. This needs to be elaborated agricultural small business management program in the with the diversity of economic, social and cultural potential village supported by the APBD, PAD and Swadaya funding and capital as well as the potential of data technology schemes has had strong links with the empowerment of available on various media, to strengthen the economic, social farmers and community business groups with Adj values. R2 and cultural village through the framework of agriculture big are 0.9759 and 0.9482, respectively. Likewise, for the poor, data analytic. the program is strongly correlated (Adj. R2 0.7040). 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