<![CDATA[The rapid development of financial technology (fintech), particularly digital wallet applications like OVO, has significantly transformed transaction patterns in society. However, issues such as server instability and unsatisfactory user experiences frequently emerge on social media platforms. This study aims to analyze user sentiments toward OVO on platform X (formerly Twitter) by comparing the performance of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Data were collected through web scraping from 1,000 Indonesian-language tweets containing the keyword "OVO." The research methodology included text preprocessing (data cleaning, tokenization, stopword removal), feature extraction using TF-IDF, and sentiment classification (positive, negative, neutral). Evaluation results demonstrated that SVM achieved the highest accuracy of 85.2%, while Naïve Bayes reached 78.5%. SVM also outperformed in precision (87%) and recall (83%) due to its ability to handle non-linear data. These findings provide actionable recommendations for OVO developers to enhance server stability and features based on user feedback. Additionally, this study serves as a reference for future sentiment analysis research employing algorithmic comparisons.]]>
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