Analysis of eFootball Game User Sentiment Using the Support Vector Machine (SVM) Method - Caca, Caca Agustian; April Lia Hananto; Fitria Nurapriani; Baenil Huda (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
This study examines the analysis of user review sentiment for eFootball games on the Google Play Store using the Support Vector Machine (SVM) method. A total of 900 reviews written in Indonesian were taken and collected, and divided based on user ratings. The research process includes data exploration, text cleanup (preprocessing), sentiment labeling based on rankings, modeling using SVM, and model evaluation with confusion matrix and accuracy metrics. The results of the analysis showed that the majority of reviews conveyed positive sentiment (48.7%) followed by negative sentiment (44.9%) and neutral sentiment (6.4%). The SVM-based model built in this study achieved an accuracy of 76%, with adequate precision, memory, and F1 scores, especially in the positive and negative sentiment categories. These findings suggest that SVM is effective in classifying digital game review sentiment, but performance in the neutral category requires significant improvement. The study contributes to the use of machine learning to analyze user perceptions of eFootball games and provides recommendations for developers to improve product quality through automated sentiment analysis.