Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree - Muhammad Rizky Mawandhyka Akbar; Irfan Pratama (Universitas Islam Indragiri, 2025)
In today’s era of globalization, rapid technological advancements are driving innovation across various sectors, including the banking industry. One of the key digital innovations in banking is mobile banking (m-banking), which allows customers to perform transactions via smartphones. This study aims to analyze the sentiment of user reviews on the MyBCA application using three classification methods: Naive Bayes, Random Forest, and Decision Tree. A total of 5,000 user reviews were collected from the Google Play Store through web scraping techniques. The data was preprocessed using the TF-IDF weighting method and processed with Python programming language and the Scikit-Learn library. The dataset was split into 90% training data and 10% testing data. This study also applies the ISO 9126 standard for multi-label classification to assess software quality based on Usability, Efficiency, Functionality, Reliability, and Maintainability. Evaluation results indicate that Random Forest achieved the highest accuracy at 94.09%, outperforming Naive Bayes (81.77%) and Decision Tree (82.38%). This research contributes to the development of a sentiment-based evaluation method for mobile banking applications, integrating user feedback analysis with ISO 9126 quality standards, and offers a useful reference for improving digital banking services.