PERBANDINGAN KINERJA METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI PINTU - Obos, Achmad Ariesta; Rahim, Abdul; Arbansyah, Arbansyah (Jurnal Informatika dan Teknik Elektro Terapan, 2025)
The Pintu app is a cryptocurrency investment platform in Indonesia designed to buy, sell, store and send cryptocurrencies easily. The app targets the younger generation and retail investors through its ease of use and user-friendly features. This study compares the performance of two machine learning methods, Naïve Bayes and Support Vector Machine (SVM), in sentiment analysis of Pintu app user reviews. 10,000 user review data were collected through web scraping and labelled as positive or negative sentiment. The preprocessing stage includes Case Folding, Stopword Removal, Tokenizing, and Stemming, followed by TF-IDF transformation for data vectorisation. Data imbalance is addressed using the SMOTE technique, and model validation is performed using K-Fold Cross Validation. The results show that SVM has superior performance compared to Naïve Bayes. SVM achieved 92.55% Accuracy, 93.20% Recall, and 95.06% F1-score, while Naïve Bayes recorded 91.50% Accuracy, 91.07% Recall, and 94.28% F1-score. However, Naïve Bayes excelled in Precision, with a value of 97.71% compared to SVM at 97%. Overall, SVM showed an optimal balance between Accuracy, Recall, and F1-score, making it the more effective method for sentiment analysis of Pintu app user reviews.