Economic Structure-Based Clustering and Development Strategy Formulation for Indonesian Provinces Using K-Means and Entropy Weight-TOPSIS
Abstract
The development gap among provinces in Indonesia highlights the need for an approach that consider the differences in the economic structure of each province. This study aims to group Indonesian provinces based on their economic structure, evaluate development performance, identify key performance gaps and their potential root causes, and formulate specific improvement strategies. K-Means clustering is used to identify provinces with similar economic structures, Entropy Weight – TOPSIS is used to evaluate provincial performance, and weighted gap analysis is used to identify indicators that significantly contribute to the development performance gap. Fishbone analysis is used to explore potential root causes that support the formulation of improvement strategies. The analysis resulted in five clusters of Indonesian provinces: manufacturing, agriculture, mining, metropolitan services, and tourism and public services. Metropolitan services cluster has the best performance based on the TOPSIS Score (0.693), followed by manufacturing (0.427), mining (0.159), agriculture (0.113), and tourism and public services (0.092). Pembentukan Modal Tetap Bruto (PMTB) is the main contributor to the performance gap in the manufacturing cluster (45.49%) and mining (50.65%), internet penetration in the agriculture cluster (38.42%), and poverty rate in the tourism and public services cluster (52.27%). Root cause analysis highlights issues related to investment capacity, digital connectivity, infrastructure accessibility, productivity, employment, and poverty. Specific strategies are recommended to address the performance gaps among provinces in Indonesia.
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DOI: https://doi.org/10.31284/j.jasmet.2026.v7i2.9416
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