Sentiment Analysis of Action Mobile Application Reviews Using Logistic Regression and Support Vector Machine - Haniful Fikri; Nurdin; Nunsina (Universitas Medan Area, 2026)
The digital transformation has driven PT. Bank Aceh Syariah to launch the Action Mobile application. Despite its benefits for customers, user reviews on the Google Play Store indicate varying perceptions due to differences in digital experiences. This study aims to analyze user sentiment toward the Action Mobile application while comparing the effectiveness of the Logistic Regression and Support Vector Machine (SVM) algorithms. A total of 3,000 clean review data were collected through web scraping techniques. The dataset exhibits an imbalanced distribution, dominated by 1,840 positive reviews (61.33%), followed by 821 negative reviews (27.37%), and 339 neutral reviews (11.30%). Model testing was conducted using the 10-Fold Cross Validation so that each data has the opportunity to become test data and the evaluation results become more objective, utilizing TF-IDF for word weighting. The evaluation results using a 3 × 3 multiclass confusion matrix based on a weighted average demonstrate that the Logistic Regression algorithm outperforms SVM across all testing metrics. The Logistic Regression model successfully achieved an Accuracy of 0.9023, Precision of 0.897, Recall of 0.9023,, and an F1-Score of 0.895. Meanwhile, the SVM model obtained an Accuracy of 0.9013, Precision of 0.8969, Recall of 0.9013, and an F1-Score of 0.898. This performance variance proves that the Logistic Regression architecture is more adaptive and optimal for this specific case study. The findings of this study are expected to serve as evaluation material for enhancing Action Mobile services.