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All Journal Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) JURNAL GALUNG TROPIKA Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Pengabdian Magister Pendidikan IPA MAT-EDUKASIA Spatial : Wahana Komunikasi dan Informasi Geografi Jurnal Ilmiah Manusia dan Kesehatan Jurnal E-Bis: Ekonomi Bisnis Economics and Digital Business Review SIGANUS: Journal of Fisheries and Marine Science Literatify : Trends in Library Development Jurnal Ilmiah Mahasiswa Raushan Fikr Carita : Jurnal Sejarah dan Budaya Prosiding Seminar Nasional dan Call For Paper Kebidanan Universitas Ngudi Waluyo Jurnal Healthy Mandala Waluya Jurnal Pengabdian Masyarakat Multidisciplinary Indonesian Center Journal Jurnal Akuntansi dan Keuangan Teknofile, Jurnal Sistem Informasi
Hasmawati
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Journal : Indonesian Journal on Computing (Indo-JC)

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Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates Primanda Sayarizki; Hasmawati; Nurrahmi, Hani
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.934

Abstract

In this modern era, Indonesian society widely utilizes social media, particularly Twitter, as a means to express their opinions. Every day, various opinions of Indonesian citizens are disseminated on this platform, including their views on prospective presidential candidates for the year 2024. Analyzing public opinions regarding prospective presidential candidates in 2024 is crucial to understanding the sentiment of the people toward these candidates. Such sentiment analysis can be conducted using deep learning techniques such as IndoBERT to acquire knowledge regarding the classification of sentiments as positive, neutral, or negative. IndoBERT is employed to generate vector representations that encapsulate the meaning of tokens, words, phrases, or texts. These representation vectors can then be input into a classification model to perform sentiment analysis. The sentiment classification model undergoes testing with a diverse set of tweets in the test dataset, which represent a wide range of public opinions. The evaluation results indicate an overall accuracy rate of 80%, with precision rates of 62% for negative sentiment, 81% for neutral sentiment, and 85% for positive sentiment. Additionally, the recall rates for each sentiment are 64% for negative, 81% for neutral, and 84% for positive, with corresponding F1-scores of 63%, 81%, and 85%, respectively.
Co-Authors Abd. Firman Bunta Ade Romadhony Ade Susanti Aflaha Rahayu Ahdar Ali, Leniarti Ali, Muh. Arif Andi Basru Wawo Andi Muhammad Fuad Ramadhan Andriani, Asti Azzahra, Ardillah Bunyamin Djumat, Irwan Doki Tamario Fauziah Bachtiar Ferinaldi Fitriani Umar Hamzah Hamzah, Nurmiaty Handriani Hani Nurrahmi Hanis Permayanti Harianto Haruddin Hasriani Herawaty Hibali, Chintya Mulia F Laode Husniati Husniati Ihksan, Abdul Jamal Saripa Juandri, Henzi Khairul Rizal Malik, Marwati Abd. Marwis Mas’ud Mochammad Imron Awalludin Muhammad Arsyad Muhammad Iqbal Muhammad Sidin Ali Musyarif Nasdwiana Primanda Sayarizki Riadi, Baso Syarief Rosya, Salsabila Saafi, La Ode Sapta Aji, A. Sugeng Sari, Iin Daryani Anugrah Syahruramadhan Syamsiah T Syamsuddin Trio Wira Dharma Usman Veftisia, Vistra Widiyanti Yusran, Sartiah