Naïve Bayes Optimization by Implementing Genetic Algorithm in Sentiment Analysis of BCA Mobile Reviews

The development of the digital era has encouraged the adoption of mobile banking applications that facilitate banking transactions, including the BCA Mobile application which is simple but still adheres to a slightly outdated, user-friendly appearance but to provide the best service, it is necessary to evaluate the various problems that arise through review analysis. This study aims to conduct sentiment analysis of BCA Mobile application reviews taken from the Google Play Store, with data totaling 1,200 reviews scraping results using Google Collaboratory python programming language, to categorize negative and positive reviews used manual labeling for more accurate results, the Naïve Bayes approach is used in classifying positive and negative category reviews due to the ability of this algorithm to handle text data. However, the weakness of Naïve Bayes which is sensitive to irrelevant features can cause a decrease in accuracy. This research implements Genetic algorithm to improve the performance of Naïve Bayes. The results showed that the application of Genetic algorithm successfully increased the accuracy, precision of Naïve Bayes classification 95%, precision 92% to accuracy 98%, precision 99%, which proved the effectiveness of Genetic algorithm in optimizing the model and improving the quality of sentiment analysis.