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All Journal Jurnal Penelitian dan Evaluasi Pendidikan Jurnal Penelitian dan Pengembangan Pendidikan Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Dinamika Ilmu FENOMENA Indonesian Journal of English Language Teaching and Applied Linguistics Indonesian TESOL Journal Indonesian Journal of EFL and Linguistics Tarbiyah Wa Ta'lim: Jurnal Penelitian Pendidikan dan Pembelajaran Jurnal Ilmiah Widya Borneo DINAMIKA ILMU: Jurnal Pendidikan Southeast Asian Journal of Islamic Education FENOMENA: Jurnal Penelitian Borneo Journal of Language and Education Al'Adzkiya International of Computer Science and Information Technology Journal SIMAS JURNAL PENGABDIAN MASYARAKAT Indonesian Journal of English Language Teaching and Applied Linguistics
Widya Noviana Noor
Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Comparing Naïve Bayes and SVM for Public Sentiment on Online Gambling Policies Siti Qomariah; Widya Noviana Noor; Nazwa Risanti
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v7i1.381

Abstract

Online gambling has become a major digital and social issue in Indonesia, prompting the government to implement various enforcement policies such as website blocking, account suspension, and advertisement filtering. However, illegal gambling content continues to circulate widely on social media platforms, generating diverse public reactions. This study aims to analyze public sentiment toward online gambling policies in Indonesia using Natural Language Processing (NLP) techniques and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms. Data were collected from Twitter (X), Facebook, and Instagram, resulting in 1,110 social media posts. The dataset was processed through text preprocessing, stemming, stopword removal, and TF-IDF feature extraction. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied before model training. The experimental results show that SVM achieved better performance with an accuracy of 91.84%, while Naive Bayes achieved 86.39%. WordCloud and confusion matrix analyses indicate that negative sentiment is dominated by dissatisfaction with slow enforcement and persistent gambling advertisements, whereas positive sentiment reflects public support for stricter regulation. These findings demonstrate that SVM is more effective for Indonesian-language sentiment classification and can support data-driven evaluation of digital governance policies.
Co-Authors A. Faizul Mubarak Aman Aman Ananiah Ananiah Armella, Rega Aulia, Riskiya Aulya, Sapna Farah Bagus Prasetyo Clevara, Yayas Dani, Sekar Arum Darwis Darwis Diah Safitri Dina Destari Faizatul Widat Fariska Amelia Chaerani Hadi, Saipul Hafi Nanda Khillatul Wafiroh Komariah, Anis Kordja, Baharuddin La Ode Sanu, La Ode Mila Safitri, Mila Mohamad Muklis Muhammad Luthfi Muttaqin, Muhammad Ridho Nazwa Risanti Nurul Hayati Paramma, Muhammad Azwar R., Ahmad Auliya Rostanti Toba Samu, La Ode Sari Agung Sucahyo Shafa Shafa Sherly Yuandari Siti Qomariah Sitti Hadijah, Sitti Umar Fauzan Widi Wulansari, Widi Yuandari, Sherly Zulfikar, M.