Text Classification Using TF-IDF and Naïve Bayes: Case Study of MyXL App User Review Data - Nurhayati, Nurhayati; Hartimar, Lima; Manza, Yuke; Siregar, Kiki Putriani (PT. Technology Laboratories Indonesia (TechnoLabs), 2025)
The MyXL application, developed by leading Indonesian operator XL Axiata, allows customers to independently manage their telecommunication services. However, a significant volume of negative user reviews necessitates a deeper analysis of user sentiment. This research classifies MyXL app reviews using the TF-IDF (Term Frequency-Inverse Document Frequency) method for feature extraction and the Naïve Bayes algorithm for sentiment classification, implemented via a Python-based GUI. The study's objective is to categorize reviews into positive, negative, and neutral sentiments. A dataset of 1000 user reviews from Kaggle underwent comprehensive preprocessing—including text cleaning, normalization, tokenization, stopword removal, and stemming—before conversion into a numerical representation using TF-IDF. The classification model, built with the Naïve Bayes algorithm, was evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved an accuracy of 61.5%. This finding demonstrates that combining TF-IDF and Naïve Bayes is effective for classifying sentiment in Indonesian text reviews, particularly within the mobile app domain. Furthermore, the methodology shows clear potential for development into a large-scale and automated user opinion analysis system.