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All Journal PROSIDING SEMINAR NASIONAL (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Sistem dan Informatika Jurnal Eksplora Informatika JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Applied Information System and Management WIDYABHAKTI Jurnal Ilmiah Populer Media Aplikom Prosiding University Research Colloquium Jurnal Pengabdian Informatika (JUPITA) Bulletin of Social Informatics Theory and Application Jurnal Nasional Teknologi Informasi dan Aplikasinya
Ida Ayu Gde Suwiprabayanti Putra
Universitas Udayana
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisis Sentimen Ulasan Aplikasi dengan Multinomial Naïve Bayes, Logistic Regression, dan SVM Rahelita Pasaribu; Ida Ayu Gde Suwiprabayanti Putra
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p08

Abstract

The swift uptake of mobile health applications has led to an increase in user-generated feedback, providing important insights into public satisfaction. To explore user sentiments, this study analyzes 9,848 reviews from a health-oriented application utilizing three machine learning methods: Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). The feedbacks were classified as positive or negative. The methodology included standard preprocessing such as cleaning and stemming, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), and addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE). Models were fine-tuned and verified through 5-fold cross-validation. Effectiveness was measured by accuracy, precision, recall, and F1-score. Logistic Regression and SVM reached the greatest accuracy at 92%, while Naïve Bayes trailed at 86%. Logistic Regression showed strong precision (95%) and recall (94%) for positive reviews, with SVM performing comparably. These results emphasize the capability of sentiment analysis in enhancing digital health services through information-based user feedback.
Co-Authors -, Daniel Surya Wijaya A.A. Raka Jayaningsih Agus Muliantara Bagus Ajie Satria Frady Cakra Negara, Anak Agung Ngurah I Gusti Agung Ngurah Trisna Jayantika I Gusti Ayu Sri Melati I Gusti Putu Wisnu Wardhana I Kadek Krisna Dwi Payana I Ketut Gede Suhartana I Komang Ari Mogi I Made Adika Bhavanta I Putu Gede Hendra Suputra I Wayan Gede Adi Palguna I WAYAN SANTIYASA Ida Putu Ari Jayadinanta Jayaningsih, A.A. Raka Kadek Andre Suryana Kartika Maharani, Ida Ayu Bintang Kompiang Gede Sukadharma Lidya Elisabet Theogracia Silitonga Luh Arida Ayu Rahning Putri Luh Arimas Pertiwi Luh Gede Astuti LUH PUTU SAFITRI PRATIWI Made Rahayu Setyaningrum Maharani Putri Suari Muhammad Sahi Ni Luh Ayu Kartika Yuniastari Sarja Ni Made Dwijayani Nyoman Ayu Nila Dewi Putra, Ananda Putu Eny Suhardiyani Putu Putri Pratiwi Rafif Jhordi Raharja, Made Agung Rahelita Pasaribu Ririmasse, Charles Alexander Vidiadivani, Wahyu Wismagatha, Pande Gede Dani