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October 20, 2025Data & MetadataOpen Access

Geospatial Clustering of Potential Tourist Locations Using the K-Means Algorithm: A Case Study of Unesco Global Geopark (Sukabumi, Indonesia)

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Authors

NANunik Destria AriantiRHRahmat HidayatAEAdhitia Erfina

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Overview

Analysis of tourist locations in Sukabumi reveals distinct clusters, suggesting targeted resource allocation and development strategies.

Key Points

  • Three tourism clusters—developed, developing, and emerging—were identified using the k-means algorithm.
  • The elbow method and silhouette score were used to assess clustering quality, revealing moderate cohesion and separation.
  • Spatial patterns indicate distinct geographical distinctions between tourist clusters in Sukabumi.
  • Data-driven insights support targeted promotion and sustainable economic growth strategies in the region.

Cite This Study

Arianti et al. (2025) studied this question.

synapsesocial.com/papers/68f58f68ece7a5b64f471505https://doi.org/10.56294/dm20251206
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