Currently, tourists can submit reviews about their travel experiences through various platforms. These tourist reviews can influence and consider other potential tourists who will visit these attractions. One of the popular travel platforms is TripAdvisor. Reviews on this platform regarding Mount Bromo tourism objects are analyzed further so that the tourism object managers can get information that can be used as the basis for developing the tourism objects they manage. Sentiment analysis with a classification approach with a supervised learning algorithm can be used as a method to explore tourist sentiment which is positive or negative sentiment. Of the three classification algorithms tested in this study, the Decision Tree algorithm has the highest accuracy rate of 91%, followed by Naïve Bayes and Logistic Regression, each with 88%. The precision, recall, and f1measure levels for the Decision Tree are 0.95, 0.62, and 0.68, respectively. From the results obtained, the performance of the classification model needs to be improved because the classification model tends to predict positive sentiment class.