<![CDATA[Small and Medium Enterprises (SMEs) play a crucial role in job creation, regional competitiveness, and economic equity. In the tourism sector, particularly in ecotourism and cultural tourism, clustering SMEs presents challenges due to complex and interrelated data variables. This study aims to evaluate the effectiveness of three clustering algorithms—K-Means, DBSCAN, and Hierarchical Clustering—in segmenting SMEs based on real-world tourism datasets. A purposive sampling method was applied to 203 valid respondents from SMEs in Rembang Regency, Central Java. Clustering performance was assessed using the Silhouette Coefficient and Davies-Bouldin Index, while computational efficiency and scalability were analyzed through execution time and memory usage. The results show that DBSCAN achieved the best clustering quality (Silhouette Coefficient: 0.5496, Davies-Bouldin Index: 0.3298), effectively managing noise and irregular cluster shapes. Hierarchical clustering offered moderate quality and helped reveal relationships between SMEs. In contrast, K-Means demonstrated the lowest quality (Silhouette Coefficient: 0.2321) due to its limitation in handling non-spherical clusters. For computational efficiency, Hierarchical Clustering required the least memory (0.14 MB) and shortest execution time (5.73 seconds), while K-Means took the longest time (26.00 seconds). DBSCAN consumed more memory due to density-based processing. K-Means was the most stable in scalability testing with increasing dataset sizes, whereas Hierarchical Clustering showed inefficiency. The findings support selecting appropriate clustering methods based on data complexity and size. This study enhances data-driven tourism development strategies and advances clustering methodology for applied informatics. Future work may explore hybrid clustering and predictive models for deeper insights.]]>
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