Analisis Sentimen Berbasis Aspek pada Ulasan Mobile JKN di Google Play Store Menggunakan SVM
DOI:
https://doi.org/10.54367/means.v11i1.6409Keywords:
Aspect, Analysis, JKN, Sentimen, SVMAbstract
Aspect-based sentiment analysis on user reviews of the Mobile JKN application was conducted to identify user perceptions regarding interface, features and performance, as well as service aspects using the Support Vector Machine (SVM) method. The data were collected through scraping Google Play Store reviews from October 2025 to December 2025, resulting in 13,503 reviews, of which 3,659 met the criteria for aspect-based analysis. The research stages included text preprocessing, TF-IDF weighting, and classification using SVM. The evaluation was performed using two data-splitting scenarios, namely 80:20 and 70:30, to assess model performance under different proportions of training and Testing data. The results indicate that the service aspect is the most frequently discussed by users. The SVM model achieved the highest Accuracy of 96.88% for the interface aspect, 90.80% for the features and performance aspect, and 95.07% for the service aspect, with Precision and Recall values indicating good classification performance.References
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