<![CDATA[The increasing use of mobile applications in the retail industry has generated a large volume of user reviews that contain valuable insights regarding customer experience and service quality. However, the unstructured nature of these reviews requires an automated approach to extract meaningful patterns efficiently. This study aims to perform sentiment analysis on user reviews of the Indomaret Poinku application by integrating lexicon-based labeling with machine learning classification. A total of 10,000 reviews were collected from Google Play Store and processed through a series of text preprocessing steps, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using the Indonesian Sentiment Lexicon (InSet), producing three sentiment classes: positive, negative, and neutral. The labeled data were vectorized using CountVectorizer and classified using two algorithms: K-Nearest Neighbors (KNN) and Random Forest (RF). Evaluation results show that Random Forest outperforms KNN, achieving an accuracy of 82.5%, compared to 69% for KNN. Random Forest demonstrates superior performance in handling high-dimensional sparse text features and yields more stable predictions across sentiment classes. This study contributes to the growing body of research on Indonesian sentiment analysis by demonstrating the effectiveness of combining lexicon-based labeling with ensemble learning methods, offering practical implications for developers seeking to improve the quality and user satisfaction of digital retail applications.]]>
