<![CDATA[General Background: Social media platforms provide extensive public opinion data that can be utilized to understand perceptions of tourism destinations. Specific Background: YouTube comments related to Lapindo Mud tourism contain diverse viewpoints reflecting visitors’ experiences and societal responses to the site. Knowledge Gap: Limited studies analyze public sentiment toward disaster-related tourism destinations using machine learning–based text mining approaches. Aims: This study classifies YouTube user comments to identify sentiment patterns regarding Lapindo Mud tourism using TF-IDF weighting and the K-Nearest Neighbor (K-NN) algorithm. Results: From 520 labeled comments, the model achieved 78% accuracy, with higher precision and recall in identifying negative sentiment than positive sentiment. Novelty: The study integrates sentiment analysis, expert-based labeling, and tourism perception assessment to examine how digital discourse represents a disaster-turned-tourism site. Implications: Findings provide insights for tourism stakeholders and local authorities to understand public perception and inform strategies for managing the image and communication of Lapindo Mud as a tourism destination.]]>
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