Geospatial Clustering of Potential Tourist Locations Using the K-Means Algorithm: A Case Study of Unesco Global Geopark (Sukabumi, Indonesia)
DOI:
https://doi.org/10.56294/dm20251206Keywords:
Tourism Clustering, Potential Mapping, Spatial Analysis, Economic Development, Machine LearningAbstract
Sukabumi Regency, located in southern West Java, Indonesia, is home to abundant natural tourism resources. However, many of these sites remain underutilized due to limited infrastructure, insufficient promotion, and the lack of data-driven planning. Tourism is essential for local economic development, making the identification of high-potential areas critical for growth strategies. This study applied the CRISP-DM methodology to classify tourism destinations in Sukabumi using data from 17 tourist sites. Variables such as average visitor numbers, ticket prices, monthly growth rates, and area usage percentages were analyzed. After data preprocessing and normalization, the K-Means algorithm was employed for clustering. The Elbow Method determined the optimal number of clusters, and cluster quality was assessed using the Silhouette Score and Davies-Bouldin Index. Three distinct clusters were identified: developed, developing, and emerging tourism sites. The Silhouette Score of 0.226 and Davies-Bouldin Index of 1.323 indicated moderate cluster cohesion and separation. A thematic map visualized the spatial patterns, showing clear geographical distinctions between clusters. Cluster 0 (red) represented low-performing destinations, Cluster 1 (green) included high-traffic, developed sites, and Cluster 2 (blue) contained mid-level destinations with growth potential. The results provide valuable insights for regional tourism development. The study offers a data-driven foundation for targeted promotion, resource allocation, and sustainable planning. The findings demonstrate that geospatial clustering can effectively support tourism strategies tailored to local needs, contributing to inclusive economic growth in Sukabumi Regency.
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