ANALISIS SENTIMEN ULASAN APLIKASI ALFAGIFT PADA PLAYSTORE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE - Raden Raihan Nur Barkah Ramadhan; Elah Nurlelah (PT. Hasba Edukasi Mandiri, 2026)
As smartphone usage continues to increase in Indonesia, understanding user perceptions through application reviews becomes essential to capture real user experiences. This study aims to analyze user sentiment toward the Alfagift application available on the Google Play Store and classify the sentiment using the Support Vector Machine (SVM) method. A total of 1,887 user reviews were collected between November 2025 and January 2026 through web scraping techniques. The research stages included data collection, followed by preprocessing steps such as data cleaning, case folding, word normalization, tokenization, stopword removal, and stemming. Term weighting was performed using the TF-IDF (Term Frequency–Inverse Document Frequency) method, and sentiment was classified into two categories: positive (ratings above 3) and negative (ratings equal to or below 3). The results showed that 64.10% of reviews were positive and 35.90% were negative. A linear kernel SVM model was applied using an 80:20 training-testing split, achieving an accuracy of 84.92%, precision of 83.84%, recall of 82.02%, and F1-score of 82.79%. The findings indicate that SVM is effective for sentiment classification and provides valuable insights for application improvement.