<![CDATA[In the current era, a shift in consumer behavior is evident in the use of online platforms for booking tickets, involving various services such as flights, hotels, trains, buses, and entertainment. PegiPegi.com, as a rapidly growing online travel agent in Indonesia, demonstrates success by understanding the value of technology and maintaining strong partnerships. This phenomenon also impacts sentiment analysis, where users of this platform often provide reviews. This research aims to apply the Naïve Bayes classification method in sentiment analysis of PegiPegi.com reviews, focusing on understanding customer satisfaction and service improvement. By combining these approaches, the study contributes to a deeper understanding of user responses to OTA services and presents the evaluation results of the Multinomial Naive Bayes classification model with an accuracy rate of 89.5%. The high precision in the Negative class indicates the model's ability to identify negative reviews. However, there are challenges in classifying the Neutral class, suggesting potential for further improvement. Nevertheless, the F1-score of 0.522 reflects a good balance between overall precision and recall.]]>
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