<![CDATA[Along with technology development, many varied digital banking or Mobile Banking applications have emerged. Mobile Banking is an innovative result of technological advances in the banking sector and Jenius is one of them. The high level of competition urges Jenius to maintain its competitive advantage and continue to innovate so that it can continue to survive. To overcome these problems, researchers propose a sentiment analysis process to understand the wants and needs of user feedback. This research uses the Naïve Bayes Classifier algorithm as a sentiment analysis method with visualization results presented via the web. The dataset is obtained by scraping method with three sentiment categories: positive, neutral, and negative. The data processing process uses the Preprocessing method with Naïve Bayes testing performed on three configurations of training data: testing data to determine the highest accuracy. The results of this study show that the Naïve Bayes algorithm gets the highest accuracy rate of 90% with the results of positive sentiment word clouds that describe the ease of use of the application as a reference for Jenius to continue to maintain these advantages, while neutral sentiment word clouds that describe the difficulty of the verification process and negative sentiment word clouds that describe application performance that is not optimal as an improvement material for Jenius to increase satisfaction and increase customer satisfaction.]]>
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