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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Economics, Business, & Accountancy Ventura Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sisfotek Global Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science
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Journal : Jurnal Sistem informasi dan informatika (SIMIKA)

ANALISIS SENTIMEN MASYARAKAT TERHADAP KASUS JUDI ONLINE MENGGUNAKAN DATA DARI MEDIA SOSIAL X PENDEKATAN NAIVE BAYES DAN SVM As Shidiq, M Febrian; Alita, Debby
Jurnal Sistem Informasi dan Informatika (Simika) Vol 8 No 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3624

Abstract

Research conducted by analyzing public sentiment related to online gambling cases using datasets from x social media using the naïve bayes method approach and support vector machine (SVM). The analysis phase starts with data gathering or crawling, followed by data labeling, data preprocessing, and ultimately method categorization. The dataset comprises 2,866 tweets, with 1,436 classified as positive (50.12%) and 1,429 as negative (49.88%). The data before to the classification process is partitioned into training data and testing data, including 70% training data and 30% testing data. The analysis with the SVM approach yielded a classification accuracy of 83%, whereas the naïve Bayes method achieved just 79%. Upon completion of the method classification process, the subsequent phase involves visualization and assessment. During the visualization step, bar plots, word clouds, and word frequencies derived from sentiment analysis calculations are shown, alongside a visualization of words from the dataset. The investigation indicates that the SVM approach outperforms Naive Bayes in sentiment classification. The benefit of SVM resides in its capability to manage data with elevated limits and accuracy, enhancing its efficiency in discerning positive and negative thoughts. The findings of this study demonstrate that SVM is better appropriate for data exhibiting complicated distributions, whereas the Naive Bayes approach yields suboptimal results. Thus, SVM can be proposed as a more appropriate and reliable approach for similar sentiment analysis in the future.