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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) Edu Komputika Journal Journal of Information Technology and Computer Science IJID (International Journal on Informatics for Development) Jurnal Akuntansi dan Bisnis IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Computer System and Informatics (JoSYC) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Proceeding ISETH (International Summit on Science, Technology, and Humanity) Electrician : Jurnal Rekayasa dan Teknologi Elektro
Indriana Hidayah
Department Of Electrical Engineering And Information Technology, Faculty Of Engineering, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Social Sciences Other

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Journal : IJID (International Journal on Informatics for Development)

 1 
The Social Engagement to Agricultural Issues using Social Network Analysis Widiyanti, Tanty Yanuar; Adji, Teguh Bharata; Hidayah, Indriana
IJID (International Journal on Informatics for Development) Vol. 10 No. 1 (2021): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.2185

Abstract

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines.  In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development.  This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information.
Research Trend of Causal Machine Learning Method: A Literature Review Arti, Shindy; Hidayah, Indriana; Kusumawardani, Sri Suning
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09208

Abstract

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.
Co-Authors Adhistya Erna Permanasari Arti, Shindy Astrie Kusuma Dewi Bobby Rian Dewangga Cahyani Windarto Diah Priyawati Ebedia Hilda Am, Ebedia Hilda Eko Nugroho Eko Wahyu Nugroho Emi Iryanti Fachrul Budi Prayoga Fahmi Dhimas Irnawan Faizal M. Priyowibowo Hanung Adi Nugroho Indah Soesanti Intan Sulistyaningrum Sakkinah La Ode Mohamad Zulfiqar Lukito Edi Nugroho M. Arifin Hidayat M. Tsana'uddin Farid Muhammad Haris Diponegoro Nasrokhah Noviati Paulus Insap Santosa Rio Jumardi Ripto Mukti Wibowo Sanny Hikmawati Saucha Diwandari Septian Rico Hernawan Silmi Fauziati Siti Helmyati Sri Kustanti Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Tari Mardiana Teguh Bharata Adji Widiyanti, Tanty Yanuar Winarno Winarno