<![CDATA[The development of information technology has driven the growth of application-based transportation services, one of which is Grab Motor, which is widely used by the public to meet their daily mobility needs. Service quality is an important factor influencing user satisfaction levels. One source of information that can be utilized to evaluate service quality is user reviews available on digital platforms. This study aims to analyze the sentiment of Grab Motor user reviews as a basis for developing service improvement recommendations. The data used are 200 user reviews obtained from the Google Play Store. The applied method is Lexicon-Based because it is able to identify sentiment polarity without requiring training data. The research stages include data collection, preprocessing consisting of cleansing, case folding, tokenization, normalization, and stopword removal, then continued with the assignment of sentiment scores based on the lexicon dictionary to classify reviews into positive, negative, and neutral categories. The results show that 120 reviews (60%) include positive sentiment, 50 reviews (25%) negative sentiment, and 30 reviews (15%) neutral sentiment. Model evaluation using a confusion matrix yielded an Accuracy of 89%, a Precision of 89%, a Recall of 91%, and an F1-score of 90%. The research findings indicate that the majority of users responded positively to Grab Motor's services, particularly regarding the app's ease of use and speed of service. Meanwhile, negative sentiment was still found regarding fares, pickup delays, order cancellations, and app technical issues. The results of this study are expected to serve as evaluation material and a basis for decision-making in efforts to improve service quality and Grab Motor user satisfaction.]]>
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