Analisis Pengelompokan Spasial dan Profil Digitalisasi Usaha Mikro, Kecil, dan Menengah Menggunakan DBSCAN

Haris Satria Putra; Yoshua Ardy Putra

doi:10.47065/josh.v8i1.11269

Haris Satria Putra * Politeknik Negeri Jakarta, Depok, Indonesia
Yoshua Ardy Putra Politeknik Negeri Jakarta, Depok, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v8i1.11269

Keywords: DBSCAN; Spatial Clustering; MSME Digitalization; Cluster Analysis; Micro, Small, and Medium Enterprises

Abstract

This study addresses the uneven adoption of digital technology among Micro, Small, and Medium Enterprises (MSMEs) in Depok City and the limited information linking their geographic distribution with their digitalization characteristics. Such information is important for planning area-based business support, particularly when training and mentoring resources are limited. This study aims to identify the spatial distribution patterns of MSMEs in Depok City using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and describe the digitalization profiles of the resulting groups. An exploratory quantitative approach was applied to a dataset of 50 MSMEs across 11 districts. This number was used for exploratory analysis based on the available dataset and is not intended to represent the entire MSME population of Depok City. Clustering was performed exclusively using latitude and longitude coordinates with the Haversine distance metric, while digitalization was examined after cluster formation (post-hoc profiling) through indicators of social media, marketplace, and website adoption. K-Distance Graph analysis and sensitivity testing selected Eps = 2.1 km and min_samples = 4. DBSCAN identified four clusters containing 45 MSMEs, while five observations were classified as noise. Evaluation produced a Silhouette Score of 0.3223 and a Davies-Bouldin Index of 0.6698, indicating that cluster separation should be interpreted cautiously. The highest average digitalization score was found in Cluster 0 (1.72), while the lowest was found in Cluster 3 (0.20). The Kruskal–Wallis test indicated a statistically significant difference in digitalization score distributions across the four main clusters (H = 14.479; p = 0.0023). These findings provide preliminary insight into variations in digital channel adoption among geographically clustered MSMEs, but they do not establish that location causes differences in digitalization. In practice, the results may support needs assessment, collective training for geographically proximate businesses, and the prioritization of MSME mentoring. Further research should use larger and more representative samples and expand digitalization indicators to measure the intensity and quality of digital channel use.

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