<![CDATA[The development of digital applications such as Gojek has generated a wide range of user reviews that can be used to assess service quality and user satisfaction. However, the large volume and unstructured nature of these reviews make manual sentiment identification inefficient, which encourages the use of sentiment analysis techniques to automatically process user opinions. This study compares the performance of Random Forest, Support Vector Machine (SVM), and Logistic Regression in analyzing opinions from Gojek user reviews. The dataset consists of 8,091 Indonesian-language reviews obtained from Kaggle. The research process includes data preprocessing such as cleaning, removing irrelevant words, and stemming using the Sastrawi library. The reviews are then converted into numerical features using the TF-IDF method. The dataset is split into 80% training data and 20% testing data before classification using the three algorithms. Model performance is evaluated using accuracy, precision, recall, and F1-score. The results show that Logistic Regression achieves the highest accuracy of 91.84%, followed by SVM with 89.99% and Random Forest with 86.09%. In addition, Logistic Regression shows more balanced performance across positive, negative, and neutral sentiment classes based on precision, recall, and F1-score. Based on the results, Logistic Regression is identified as the most effective method for sentiment analysis of Gojek user reviews in this study. ]]>
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