Analisis Komparatif Metode Logistic Regression dan Support Vector Machine pada Sentimen Aplikasi GuitarTuna

Abstract

This study focuses on identifying sentiment trends among GuitarTuna app users while comparing the performance of Support Vector Machine (SVM) and Logistic Regression (LR) algorithms. A total of 20,339 raw data points were collected from the Google Play Store, the App Store, and questionnaires. Following a rigorous filtering process, which involved eliminating duplicate entries, empty text, and irrelevant responses, 13,615 valid data points were obtained. The analysis proceeded through text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and classification using both algorithms. Evaluation results indicate that SVM achieved an accuracy of 90.53%, a macro recall of 0.60, and a macro F1-score of 0.62. This performance was descriptively superior to that of LR, which recorded an accuracy of 90.30%, a macro recall of 0.56, and a macro F1-score of 0.59. However, McNemar's test, yielding a p-value of 0.6338, demonstrated that SVM's nominal advantage was not statistically significant. Furthermore, the limited sample size for the neutral class contributed to low prediction precision for both models within that category. Regarding review topics, positive sentiment was largely driven by user satisfaction with tuning precision, ease of use, and educational features. Conversely, negative sentiment was dominated by complaints concerning subscription costs, transaction issues, feature access restrictions, and connectivity problems. This research makes contributions in two key areas. From a practical standpoint, the topic extraction findings provide developers with concrete guidance for prioritizing feature and service improvements. From an academic perspective, the study offers comprehensive empirical evidence regarding the comparative evaluation of machine learning algorithms on cross-platform review datasets characterized by class imbalance.