Perbandingan Metode Naive Bayes, Decision Tree, dan Support Vector Machine dalam Analisis Sentimen Pengguna terhadap Aplikasi E-Wallet Dana - Muhamad Dimas Adityawarman; Windi Irmayani; Muhammad Ifan Rifani Ihsan (LKP KARYA PRIMA KURSUS, 2026)
This research is motivated by the rapid growth of financial technology in Indonesia, where the DANA application has become the most popular digital wallet (e-wallet) with over 200 million registered users . The high usage of this application results in an abundance of reviews on the Google Play Store, representing both customer satisfaction and complaints . The problem addressed in this research is how to automatically process these textual reviews and determine the best classification method among the three tested Machine Learning algorithms . This research aims to analyze and compare the accuracy performance of Naive Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms in classifying user sentiment . The method used in this research is a computational quantitative approach, utilizing a secondary data collection technique consisting of 50,000 reviews from the Google Play Store via Kaggle . The analysis process was conducted by applying five stages of text preprocessing, feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF), handling data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), data splitting (80% training data and 20% testing data), and model evaluation using a Confusion Matrix . The results showed that the Naive Bayes algorithm had the most superior performance with an accuracy rate of 80%, followed by Decision Tree and Support Vector Machine (SVM), each obtaining an accuracy of 78% . Therefore, it can be concluded that the Naive Bayes algorithm is the most optimal and stable method for conducting sentiment analysis classification on e-wallet application review text data after the class distribution is equalized.