Text Classification Using TF-IDF and Naïve Bayes: Case Study of MyXL App User Review Data

Nurhayati Nurhayati; Lima Hartimar; Yuke Manza; Kiki Putriani Siregar

Penulis

Nurhayati Nurhayati Universitas Potensi Utama Penulis
Lima Hartimar Universitas Potensi Utama Penulis
Yuke Manza Universitas Potensi Utama Penulis
Kiki Putriani Siregar Universitas Potensi Utama Penulis

Kata Kunci:

Text classification, TF-IDF, Naïve Bayes, MyXL, User review

Abstrak

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.

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