Building of Informatics, Technology and Science
Vol 7 No 1 (2025): June (2025)

Komparasi Algoritma Naïve Bayes, Support Vector Machine, dan Random Forest Untuk Analisis Sentimen Ulasan Pengguna Aplikasi CGV Cinemas Indonesia

Febriyanti, Natasya (Unknown)
Rozi, Anief Fauzan (Unknown)



Article Info

Publish Date
20 Jun 2025

Abstract

CGV Cinemas Indonesia is the official platform of the CGV cinema network, designed to facilitate users in accessing cinema services digitally. Google Play Store provides a review feature based on user experience, which can influence potential users in considering whether to use the application. This study aims to analyze user sentiment toward the CGV Cinemas Indonesia application using the Naïve Bayes, SVM, and Random Forest algorithms to classify sentiments as positive, negative, or neutral. In addition, this research seeks to compare the effectiveness of the three algorithms and identify which aspects of the service are most criticized and appreciated by customers. The dataset was collected through scraping Google Play using the Python programming language, resulting in 6,629 review data points. The results show that the accuracy of Naïve Bayes is 75.2%, SVM is 88.1%, and Random Forest is 85.8%, indicating that SVM is the most effective method for sentiment analysis in this study. This research is expected to help potential users understand sentiments toward the application and provide valuable insights for CGV Cinemas Indonesia to improve service quality.

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Journal Info

Abbrev

bits

Publisher

Subject

Computer Science & IT

Description

Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. ...