<![CDATA[Indonesia is home to a diverse range of tourist destinations, yet the classification and mapping of these locations remain a challenge in tourism management. This study aims to cluster tourist destinations in Indonesia by applying the K-Means algorithm with three distance metric variations: Euclidean Distance, Manhattan Distance, and Canberra Distance. The dataset was sourced from public data repositories and underwent preprocessing steps, including data normalization. The optimal number of clusters was determined using the Elbow Method, while the clustering results were evaluated using the Silhouette Score and Davies-Bouldin Index. The findings indicate that Manhattan Distance produced the highest Silhouette Score (0.321463), suggesting superior clustering performance compared to the other two metrics. The results of this study provide valuable insights for stakeholders in formulating strategic tourism promotion and infrastructure development efforts.]]>
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