<![CDATA[Various aspects of life have been significantly changed by rapid technological advances; this includes the banking industry, which has developed digital banking services. SeaBank is a digital banking application that allows us to do many things with our money, from saving to making online transactions with our mobile phones anytime and anywhere. By using the Support Vector Machine (SVM) method to classify user comments on the SeaBank Indonesia Instagram account into positive and negative comments, this research aims to find analytical ways to improve service quality and customer satisfaction through this sentiment. Data is processed in several stages, such as cleaning, normalization, tokenization, stopword removal, and stemming. Then the SVM algorithm is used to classify sentiment. These performance algorithms are measured by metrics such as accuracy, precision, recall, and F1 score. The results of the analysis of 1201 comment data show that 536 data are positive and 665 data are negative. The Support Vector Machine method shows an accuracy of 89%, precision of 93%, recall of 83%, and fl-score of 88%.]]>
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