An FCM–TOPSIS-Based Model for Grouping and Ranking Priority Regions for Poverty Alleviation: A Case Study of Indonesian Provinces

Abstract

Poverty in Indonesia is a multidimensional problem that reflects regional disparities and cannot be adequately represented by a single indicator. Differences in socioeconomic conditions among provinces require more targeted poverty alleviation policies. This study aims to map and prioritize poverty alleviation areas in Indonesia by integrating the Fuzzy C-Means (FCM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The novelty of this study lies in combining fuzzy clustering and multi-criteria decision-making to identify groups of provinces with similar socioeconomic characteristics and determine intervention priorities within each group. Secondary data from the Central Statistics Agency in 2025 covering 38 provinces and eight welfare indicators were analyzed. FCM was used to cluster provinces, while TOPSIS was applied to rank poverty alleviation priorities. The results produced three clusters with distinct socioeconomic characteristics. Cluster 1, consisting of Central Papua and Papua Pegunungan, showed the highest socioeconomic vulnerability. Cluster 2 represented provinces with moderate conditions, while Cluster 3 comprised provinces with relatively better development indicators. TOPSIS results identified Central Papua, West Papua, and South Papua as the highest-priority provinces in Clusters 1, 2, and 3, respectively. The integrated FCM–TOPSIS approach provides a comprehensive framework for targeted and effective poverty alleviation planning.