Klasterisasi Provinsi Di Indonesia Berdasarkan Indeks Masyarakat Digital Tahun 2025 Menggunakan Algoritma K-Means Clustering
DOI:
https://doi.org/10.47065/jieee.v6i1.3531Keywords:
Digital Society Index; K-Means Clustering; Digital Divide; Data MiningAbstract
Abstract-Digital transformation is one of Indonesia's national development priorities, yet the level of digital readiness across regions still shows a considerable gap. The 2025 Digital Society Index (Indeks Masyarakat Digital or IMD) measures each province's digitalization achievement through four pillars, namely Infrastructure and Ecosystem, Digital Literacy, Empowerment, and Employment. This study aims to group 38 Indonesian provinces based on these four pillars using the K-Means Clustering algorithm so that the pattern of regional digital disparity can be identified objectively. The dataset used is secondary data of the 2025 IMD, which was standardized using the Z score method prior to clustering. The optimal number of clusters was determined through a combination of the Elbow method and the Silhouette Coefficient, leading to the selection of three clusters with a Silhouette Coefficient of 0.262. The clustering results show that the provinces are divided into three groups, namely a digitally advanced cluster consisting of 18 provinces with an average IMD of 49.18, a developing cluster of 13 provinces with an average IMD of 43.16, and a lagging cluster of 7 provinces with an average IMD of 35.11 that is dominated by the Papua region. The Empowerment pillar was found to be the sharpest differentiator between clusters, while the Employment pillar was relatively uniform across provinces. These findings are expected to serve as a consideration for policy makers in designing more targeted digital transformation equity programs.
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