Fuzzy Geographically Weighted Clustering Analysis of Poverty Indicators in South Sulawesi, Indonesia - Annisa, Nurawalia; Aidid, Muhammad Kasim; Meliyana, Sitti Masyitah (JINAV: Journal of Information and Visualization, 2025)
Cluster analysis is a method used to group data into several clusters, where the data within a single cluster exhibit high similarity, while the data between clusters show low similarity. This study aims to classify the regencies and cities in South Sulawesi based on poverty indicators using the Fuzzy Geographically Weighted Clustering (FGWC) method. FGWC is an integration of the classical fuzzy clustering approach with geo-demographic components, incorporating geographical aspects into the analysis. As a result, the clusters formed are sensitive to environmental effects, which influence the values of cluster centers. In this study, the optimal number of clusters was determined using the IFV (Index of Fuzzy Validity) validity index, which indicated an optimal solution of three clusters. Cluster 1 consists of 9 regencies/cities characterized by a high level of poverty. Cluster 2 comprises 7 regencies/cities with a moderate level of poverty. Cluster 3 includes 8 regencies/cities with a low level of poverty.