Performance Analysis of Logistic Regression and SVM (Support Vector Machine) Algorithms on E-Football Mobile Game Review Sentiment - Athary Zikry; Farid Akbar Siregar (Journal of Science, Technology, and Innovation, 2026)
This study investigates the comparative performance of Logistic Regression and Support Vector Machine algorithms in sentiment classification of e Football mobile game reviews collected from the Google Play Store. The research employed an empirical machine learning framework involving web scraping, Natural Language Processing preprocessing, Term Frequency Inverse Document Frequency feature extraction, and supervised classification procedures. A total of 7,497 Indonesian language reviews were processed through cleaning, case folding, normalization, tokenization, stopword removal, and stemming to improve textual consistency and semantic representation. The dataset was divided into training and testing subsets using an 80:20 ratio to evaluate model generalization performance. Classification effectiveness was measured using accuracy, precision, recall, and F1 score metrics supported by confusion matrix interpretation. The findings demonstrate that Support Vector Machine achieved superior classification stability with an accuracy of 81.13% and an F1 score of 0.72, while Logistic Regression obtained an accuracy of 80.97% and an F1 score of 0.71. The results indicate that Support Vector Machine provides stronger robustness in handling high dimensional Indonesian gaming review data characterized by class imbalance and semantic variability.