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All Journal Jurnal Sains dan Teknologi Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknologi Sistem Informasi dan Aplikasi JURTEKSI Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Infotekmesin Journal of Information Systems and Informatics Journal of Technology and Informatics (JoTI) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Ikhsan, Ali Nur
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Journal of Information Systems and Informatics

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Event-Based Detection of Provocative Political Discourse on Indonesian Twitter: A Comparative Study of SVM and IndoBERT Cahyani, Evril Fadrekha; Ikhsan, Ali Nur; Astrida, Deuis Nur
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1409

Abstract

Political polarization on Indonesian social media intensified during the August 2025 House of Representatives (DPR) demonstrations, where provocative and sarcastic tweets helped amplify institutional criticism and widen public conflict. This study examines event-based automatic detection of provocative political discourse by comparing a feature-based Support Vector Machine (SVM) classifier with a transformer-based IndoBERT model on a large-scale Indonesian Twitter (X) corpus collected from 15 August to 15 September 2025. Tweets were preprocessed and labeled using a rule-based proxy lexicon to distinguish provocative from neutral content, then both models were trained and evaluated under the same experimental setting. Results show that SVM is highly effective for recognizing explicit provocation expressed through repetitive and lexically salient slogans, whereas IndoBERT provides more stable detection of implicit and context-dependent provocation, including irony and sarcasm that are common in Indonesian political talk online. In addition, temporal exploration indicates sharp spikes in tweet volume that align with key offline protest moments, suggesting a close coupling between street-level mobilization and digital discourse dynamics. Overall, the findings support the use of contextual NLP models within event-centered social media analysis to strengthen scalable monitoring of polarization and to inform early-warning approaches for escalating conflict in Indonesia’s digital public sphere.
Co-Authors Adam Prayogo Kuncoro Aeni, Alfina Nur Ajeng Widiawati, Chyntia Raras Alif Nur Fadilah Alifah Dafa Iftinani Alifian , Raditya Sani Anggraeni, Epri Astrida, Deuis Nur Cahyani, Evril Fadrekha Cahyo, Samsul Dwi Dani Arifudin Dias Ayu Budi Utami, Dias Ayu Budi Fadilah, Alif Nur Faizah, Nailatul Fatiha, Muhammad Reza Hidayat, Muslimin Iftinani, Alifah Dafa Indrawan Firdauzi Irma Darmayanti Isnaini, Khairunnisak Nur Maulana Baihaqi, Wiga Naufal Yogi Aptana Pungkas Subarkah Pungkas Subarkah Rakhmawati, Desty Rizki Wahyudi Robbani, Ayat Akras Rofiqoh, Dayana Sabaniyah, Arbangi Puput Setiawan, Ito Shafira, Lulu Siti Alvi Solikhatin Subarkah, Pungkas Suhaman, Jali Wibowo, Adlan Yuliastuti, Lulu Yunita, Ika Romadoni