Implementing the Naïve Bayes Method for Sentiment Classification of Brimo App User Reviews on the Google Play Store
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Abstract
The development of digital technology has encouraged the increasing use of mobile-based banking applications, one of which is Brimo, developed by Bank BRI. User reviews on the Google Play Store reflect the experience and satisfaction level with the application, but the large number of reviews makes the manual analysis process inefficient. This study aims to classify the sentiment of Brimo application user reviews into positive, negative, and neutral categories using the Naïve Bayes method. The data collection technique was carried out using web scraping to retrieve user reviews from the Google Play Store. The analysis process was performed using Google Colab with the Python programming language, through the stages of preprocessing, word weighting with TF-IDF, and classification using the Multinomial Naïve Bayes algorithm. The sentiment analysis results showed that the majority of user reviews were negative (78%), compared to positive reviews (22%). Classification using the Naïve Bayes Model yielded an accuracy of 73.56%, which indicates a reasonably good ability to recognize overall sentiment, but is still affected by data imbalance. The results of this study are expected to provide an overview of user perception and serve as input for developers to improve service quality. The impact of this research is the availability of an artificial intelligence-based analysis model capable of identifying user opinions automatically, quickly, and accurately in supporting the development of digital banking services.
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