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All Journal Jurnal Teknik Sipil ELT Forum: Journal of English Language Teaching Jurnal Pilar Nusa Mandiri IT JOURNAL RESEARCH AND DEVELOPMENT JMM (Jurnal Masyarakat Mandiri) Jurnal Nasional Komputasi dan Teknologi Informasi Abdi Masyarakat JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat TIN: TERAPAN INFORMATIKA NUSANTARA Community Empowerment Bulletin of Computer Science Research Jurnal Sains Student Research Jurnal of Information Technology and Society (JITS) Jurnal Ilmiah Nusantara JURNAL AKADEMIK EKONOMI DAN MANAJEMEN Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer HUMAN: Journal of Social Humanities and Science
Yudistira Bagus Pratama
Computer Science Study Program, Universitas Muhammadiyah Bangka Belitung, Indonesia
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Journal : Jurnal Ilmiah Nusantara

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PERBANDINGAN PERFORMA ALGORITMA NAIVE BAYES DAN SVM UNTUK ANALISIS SENTIMEN KOMENTAR YOUTUBE TERHADAP INDUSTRI ESPORTS DI INDONESIA Tito Dian Permana; Yudistira Bagus Pratama; Zikri Wahyuzi; Eka Altiarika; Arvi Pramudyantoro
JURNAL ILMIAH NUSANTARA Vol. 2 No. 6 (2025): Jurnal Ilmiah Nusantara
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jinu.v2i6.6753

Abstract

The esports industry in Indonesia is rapidly growing and gaining significant attention on social media, particularly YouTube, where comments reflect public perceptions. This study compares the performance of Naive Bayes and Support Vector Machine (SVM) in classifying sentiments from YouTube comments and explores key themes using Latent Dirichlet Allocation (LDA). Data were collected via the YouTube Data API v3, labeled with TextBlob and manually verified into positive, negative, and neutral categories. After preprocessing and TF-IDF representation, class imbalance was handled with SMOTE, and models were trained and evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Results indicate that Naive Bayes achieved 73.85% accuracy with an F1-score of 0.71, while SVM slightly outperformed with 73.97% accuracy and the same F1-score. SVM showed better consistency in classifying negative and neutral comments, whereas Naive Bayes was more effective for positive ones. LDA revealed dominant discussion topics such as appreciation, enthusiasm, community interaction, criticism, and support for esports development. These findings highlight SVM’s superior overall performance and the value of LDA in uncovering public discourse, providing both academic contribution and practical insights for the esports industry in understanding public sentiment.
Co-Authors -, Ryevilgo Christivo Agil, Sofwan Arif Agustine, Putri Cahyani Al Ahfaz Reza Ramdani Altiarika, Eka Andesta Granitio Irwan andriani, riski Ari Ardiansyah Ari Gunawan Arif, Maulana Arnola, Micha Arvi Pramudyantoro Azzani, Arlina Feijriah Bayu Saputra Dalimunthe, Nurzaidah Putri Eka Altiarika Elda, Elda Emikawati Erick Prayogo Walton Erick Prayogo Walton, Erick Prayogo Febriyanti, Wiwit Februari, Fani Fermanda, Syandhu Dea Haiyudi Haiyudi Haiyudi Haiyudi Haiyudi, Haiyudi Hendrik, Maulina Hevitria Hevitria Iful Rahmawati Mega, Iful Rahmawati Marwani Marwani, Marwani Marzuq, Jauhar Ridloni Mega Sukma Muhamad Mustaqim Muhammad Mustaqim Nabela, Silvio Juliana Noviani, Ega Nurjanah Nurjanah Okta, Evan Dwi Pratama, Farhan Aflah Pratama, Suprayuandi Putri, Elsa Hana Rahma Ramdani, Al Ahfaz Reza Randi Atul Aufa Ratno Purnamasari Safitri, Ananda Dwi Setiawan, Rifki Hanif Sevtian, Andre Silvio Juliana Nabela Sitthipon Art-in Suheni Suheni Suheni Suprayuandi Pratama Suprayuandi Pratama Tito Dian Permana Vebrian, Rajab Wahyuzi, Zikri Wardani, Shinta Pramudya Yuniarni Yuniarni Yurdayanti Yurdayanti Yurdayanti, Yurdayanti Zaliman, Iski Zikri Wahyuzi