Penerapan Penyeimbangan Data Pada Analisis Sentimen Ulasan Game Magic Chess Go Go di Play Store dengan Naive Bayes - Mustaqim, Muhammad Hafizd; Santoso, Angga Bayu (Building of Informatics, Technology and Science (BITS), 2025)
This study aims to perform sentiment analysis on reviews of the Magic Chess Go Go game from the Google Play Store, which exhibits data imbalance with 2,949 negative sentiment entries and 1,537 positive ones. To address this issue, a sentiment classification model was developed using the Naïve Bayes algorithm, along with a comparison of four data balancing methods: SMOTE, ADASYN, Random Oversampling (ROS), and Random Undersampling (RUS). Evaluation was conducted using a confusion matrix under two data splitting schemes, with the 80:20 split yielding the best performance. In this scheme, SMOTE achieved the highest accuracy at 84.2%, followed by ADASYN (83.8%), ROS (82.9%), and RUS (77.9%). These results indicate that SMOTE is the most effective method for handling data imbalance in this context. It can be concluded that applying SMOTE to the Naïve Bayes model in the 80:20 split scenario provides the best performance, demonstrating that synthetic data generation through SMOTE helps balance the dataset without significant information loss. Future work may explore alternative algorithms and parameter tuning to enhance sentiment classification performance.