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All Journal Jurnal Pendidikan Teknik Mesin Profesional Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran Sainteknol : Jurnal Sains dan Teknologi Jurnal Rekayasa Mesin Automotive Science and Education Journal Journal of Mechanical Engineering Learning Sistemasi: Jurnal Sistem Informasi Unnes Science Education Journal Journal of Innovative Science Education Aiti: Jurnal Teknologi Informasi Jurnal Pendidikan dan Teknologi Indonesia Jurnal Inovasi Mesin Journal Of Information System And Artificial Intelligence Abdi Teknoyasa Jurnal Manajemen, Akuntansi dan Logistik (JUMATI) PESARE: Science and Engineering Service Journal The Indonesian Journal of Computer Science Jurnal Abdimas Ulil Albab
Widi Widayat
Jurusan Teknik Mesin, Fakultas Teknik, Universitas Negeri Semarang
Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Public Health Social Sciences

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Journal : Journal Of Information System And Artificial Intelligence

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A Comparative Study of Naïve Bayes, SVM, Random Forest, and LSTM Performance in Sentiment Analysis on a Movie Review Dataset Widayat, Widi
Journal Of Information System And Artificial Intelligence Vol. 7 No. 2 (2026): Vol.7 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 - Rusiyanto Aditama Farhan Mudhoffar, Rama Reiswa Aji Kusumo Atmojo, Aji Al Janan, Dony Hidayat Apriliana, Cindi Dila Aris Budiyono Aris Setya Endra Gunawan, Aris Setya Endra Arya Adi Saputra, Fernando Boenasir - Budiansyah, Dadan Budiarso Eko Budiman, Kholiq Bunyamin Bunyamin Candra Wahyu Sportyawan, Candra Cris Kuntadi Dedi Gunawan Dewi Mardika, Shela Dwi Widjanarko Fauzun Fauzun Hafsha, Amelia Risanti Harizal Rizki Ramadhian, Harizal Rizki Harwin Saptoadi Hendrix Noviyanto Firmansyah, Hendrix Noviyanto Karnowo - Kasmui -, Kasmui Kurniasih, Mia M. Nur Al Khadik, M. Nur Margareta Rahayuningsih Nugroho Edi K Margunani, Margunani Minallah, Aldin Nasrun Mundzir, Ahmad Muslimawati, Dinny Patria Nathan Hindarto Nirot A, As'ad Pambudi, Mada Bayu Pratama, Tegar Unggul Pratiwi, Triana Surya Prima Astuti Handayani Pujasakti, Dwi Pujasakti, Dwi R Luki Karunia Riski Yustiar Prabowo, Riski Rusiyanto Rusiyanto, Rusiyanto S A, Juanita Tria Sari, Fajri Diannita Sri Sukaesih Syafei, M Hilman Gumelar U, Ihsan Cahyo Wahyudi Wahyudi Wara Dyah Pita Rengga Widya Aryadi Wiyanto - Yasir Sidiq Yudi Wahyu Wibowo