<![CDATA[Banking services using mobile banking applications, including Indonesian state bank (called BRI). A study on feedback regarding BRI services based on mobile applications was done. In order to compete with other banks, that is used to enhance and modernize the quality of BRI services provided to clients. Based on phenomena that occur in these situations. This study aims to classify comments from users of the BRI Mobile Banking Application on Google Play services into positive and negative comment sentiments. In this study, the Support Vector Machine (SVM) technique is utilized to determine between positive or negative reviews. The sentiment analysis of BRI google play data was carried out by comparing the Radial Basis Function (RBF) kernel function and the Linear kernel. As well as the experiment of adding feature selection, parameters, and n-grams for a period of two years, from January 1st,, 2017 to December 31st, 2018. The results of the study using the k-fold cross-validation test, the precision value of the SVM kernel linear is 90.80 percent and the SVM kernel RBF is 90.15 percent. In the RBF kernel, there are 1,816 positive classes and 1,455 negative classes. While the Linear kernel obtained a positive class of 1,734 and a negative class of 1,637.]]>
