Abstract
This study explores sentiment analysis on SmartCampus Unisbank application reviews on Google Play Store using the Naive Bayes classification method. Through Python programming language and web scraping techniques employing the google-play-scraper library, review data was automatically obtained and organized in CSV format. Text preprocessing techniques such as case folding, stopwords removal, tokenization, and stemming were applied to ensure accurate analysis. The data was divided into training and testing sets, and TF-IDF (Term Frequency-Inverse Document Frequency) was used for feature extraction. A Naive Bayes model was constructed and evaluated, achieving an accuracy of 84.6%. While the model demonstrated proficiency in identifying negative sentiments with 100% precision, it requires refinement for recognizing positive sentiments. These findings provide valuable insights for SmartCampus Unisbank developers to understand user perspectives and improve application quality
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