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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ekonomi Manajemen Sistem Informasi Budimas : Jurnal Pengabdian Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal Of Information System And Artificial Intelligence Abdi Teknoyasa Jurnal Pendidikan Matematika The Indonesian Journal of Computer Science Ulil Albab International Journal of Marine Engineering and Applications
Widi Widayat
Fakultas Teknologi Industri, Universitas Islam Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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A Comparative Study of Naïve Bayes, SVM, Random Forest, and LSTM Performance in Sentiment Analysis on a Movie Review Dataset Widi Widayat
Journal Of Information System And Artificial Intelligence Vol. 6 No. 2 (2026): Vol.6 No. 2 (2026): Journal of Information System and Artificial Intelligence V
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v7i2.274

Abstract

Sentiment analysis is an important task in natural language processing, aimed at identifying and classifying opinions or emotions in textual data. This study compares the performance of four classification algorithms—Naïve Bayes, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM)—on 25,000 English-language movie reviews with balanced sentiment labels. Text preprocessing includes cleaning, tokenization, and TF-IDF vectorization for traditional models. For LSTM, both randomly initialized embeddings and pre-trained embeddings are tested. Results, evaluated using accuracy, F1-score, and confusion matrix, show that SVM performs best with 89% accuracy, followed by Naïve Bayes and LSTM at 86%, and Random Forest at 82%. LSTM performs poorly with TF-IDF or self-trained embeddings but improves significantly with pre-trained embeddings. These findings indicate that traditional models, especially SVM, remain highly effective for sentiment analysis on moderately sized datasets, while LSTM requires proper text representation to perform competitively.
Co-Authors Abdul Haris Rosyidi Aen Hendrawati Agus Triyono Ahmad Mundzir Anindya Ardiansari Badrus Zaman Bayu Tirta Danang Dwi Saputro Diah Priyawati Dian Purworini Feddy Setio Pribadi Helmi Imaduddin Ihsan Cahyo Utomo Karnowo Maryam Mochamad Arief Budihardjo Muhammad Halim Maimun Neneng Sri Rahayu Nurandani Hardyanti Nurlaeli Sri Purnomowati Reva Berliani Syahla D.A Rona Rizky Bunga Chasanah Sandra Anggita Sari Sidiq Setyawan Sri Handayani Vivi Andela Mandasari WAHYUDI wiyanto, Agung Yazid 'Azza Ghony Gunawan