Perbandingan Algoritma Machine Learning Untuk Sentimen Analisis Game Bus Simulator Indonesia - Oktafiandi, Hery; Winarnie, Winarnie (Universitas Asahan, 2024)
This study aims to perform sentiment analysis on user reviews of the Bus Simulator Indonesia (BUSSID) application, developed by Maleo from Surabaya and released in 2017. A total of 10,000 reviews were collected from the Google Play Store using web scraping techniques and labeled based on ratings; reviews with ratings above 3 were considered positive, while those with ratings of 3 or below were considered negative. The reviews were then processed through case folding, cleaning, tokenizing, stopword removal, and stemming, and their features were extracted using the TF-IDF method. The data was split into 70% for training and 30% for testing. Three machine learning algorithms were applied: Naive Bayes Classifier (NBC), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM). The results showed that SVM had the highest accuracy at 79%, followed by SGD at 77%, and NBC at 76%. Evaluation using metrics such as accuracy, precision, recall, and f1-score indicated that this analysis provides valuable insights for BUSSID developers to improve the application’s quality. sentiment analysis,