Sentiment Analysis of the Matahari Application to Provide User Experience Insights using Support Vector Machine - Moch Arif Samsul Rizal; Anik Vega Vitianingsih; Hewa Majeed Zangana; Anastasia Lidya Maukar; Fitri Marisa (Building of Informatics, Technology and Science (BITS), 2025)
The expansion of Indonesia's digital commerce ecosystem has pushed retail companies to strengthen the quality of their online services to remain competitive. Matahari, one of the country's leading retail brands, launched its mobile app as a platform for shopping, promotions, and customer interaction. However, user feedback on the Google Play Store indicates persistent problems with system responsiveness, ease of use, and the consistency of promotional information. This study examines sentiment patterns in 2,500 user reviews and classifies them using a Support Vector Machine (SVM) based model that incorporates three kernel types: Linear, RBF, and Polynomial. Before modelling, the text corpus underwent several pre-processing steps—such as tokenization, stopword filtering, and stemming represented numerically using TF-IDF weighting. Among all tested configurations, the Linear kernel produced the strongest results, achieving an accuracy rate of 88%. Despite a moderate distribution across categories (1030 negative, 886 neutral, and 584 positive), the model achieved consistent performance across all classes. Evaluation using Precision, Recall, and F1-Score confirmed the validity of the 88% accuracy without the need for additional sampling techniques. From a scholarly standpoint, this research adds insight into sentiment analysis for retail applications within the Indonesian context by applying a machine-learning approach. In practice, the outcomes highlight areas for improvement, particularly technical stability, the intuitiveness of user flows, and promotional clarity to support a better overall user experience.