Komparasi Algoritma Naïve Bayes, Support Vector Machine, dan Random Forest Untuk Analisis Sentimen Ulasan Pengguna Aplikasi CGV Cinemas Indonesia
-
Natasya Febriyanti
*
Universitas Mercu Buana Yogyakarta, Yogyakarta,
Indonesia
- Anief Fauzan Rozi Universitas Mercu Buana Yogyakarta, Yogyakarta, Indonesia (*) Corresponding Author
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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