Perbandingan Algoritma Machine Learning Untuk Sentimen Analisis Game Bus Simulator Indonesia - Oktafiandi, Hery; Winarnie, Winarnie (JurTI (Jurnal Teknologi Informasi), 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,