<![CDATA[The use of digital wallets such as DANA in Indonesia continues to increase along with the need for fast and practical non-cash transactions. User reviews on the Google Play Store are an important source of information to assess satisfaction and service problems. This study aims to classify user sentiment towards the DANA application using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. A total of 1,000 reviews were collected and processed through text cleaning, tokenization, stopword removal, and stemming. Sentiments were classified into positive, neutral, and negative using the lexicon method and expert validation. The results showed that NBC was superior to KNN, with the highest accuracy of 71.83%, while KNN only reached 56.44%. NBC was also more effective in detecting negative sentiment, although both were less than optimal for neutral sentiment. Word cloud visualization displays the dominant words in each sentiment category. The conclusion of this study states that Naïve Bayes is more effective in analyzing sentiment reviews of digital wallet applications such as DANA.]]>
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