<![CDATA[Purpose – This study aims to analyze user review sentiment toward the Gojek Driver application and compare the performance of two classification algorithms, Support Vector Machine (SVM) and Naïve Bayes, using Optuna as a framework for hyperparameter tuning.Methods – The study collected and labeled user review data into positive and negative sentiment categories. Text preprocessing involved cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Features were represented using TF-IDF. The dataset was then divided into training and testing sets, and SVM and Naïve Bayes models were trained using automated hyperparameter optimization with Optuna. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix.Findings – The application of SMOTE to the Optuna-tuned SVM model produced better performance than the other models tested in this study. The best model achieved an accuracy of 0.868, a highest cross-validation accuracy of 92.72%, and a weighted average F1-score of 0.87. These results indicate that SVM was more effective in handling high-dimensional TF-IDF features and complex decision boundaries.Research implications – The findings support the use of automated sentiment analysis to assist operational decision-making and improve the quality of Gojek Driver services. The proposed approach can accelerate the identification of service-related issues and provide a basis for proactive responses to user feedback.Originality – This study offers an original contribution by directly comparing SVM and Naïve Bayes on a Gojek Driver review dataset while applying Optuna-based hyperparameter tuning. It highlights the effect of automated tuning on both algorithms within a TF-IDF representation framework for ride-hailing service data, a topic that remains underexplored in the specific context of Gojek Driver within the local literature.]]>
