<![CDATA[In today's era, technological advancements have significantly facilitated various aspects of life, including the financial sector through the emergence of financial technology (fintech). One of the widely used fintech services in Indonesia is the DANA digital wallet. The abundance of user reviews generated from the use of this application reflects the level of user satisfaction or dissatisfaction with the services provided. However, this data is generally unstructured text, making it difficult to analyze manually. Therefore, an automatic analysis method is needed to categorize the sentiments contained in these reviews. Support Vector Machine (SVM) is one of the algorithms that can be used for sentiment classification, although its effectiveness in analyzing reviews of the DANA application still requires further investigation.This study aims to analyze sentiment in user reviews of the DANA application obtained from the Google Play Store using the SVM algorithm. A total of 2,000 reviews were collected through scraping and processed through several stages, including data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Text features were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method before classification with SVM. SVM was chosen due to its ability to handle high-dimensional data and its strong performance in text classification tasks. The results indicate that SVM is capable of classifying sentiment with high accuracy, achieving around 90% or more, along with high precision, recall, and F1-score values. These findings are expected to help application developers understand user needs and complaints to enhance the quality of DANA’s services.]]>
