The Gath–Geva Algorithm for Clustering Spatial Inequality of Stunting in East Nusa Tenggara Province

Abstract

Stunting remains a critical public health issue in Indonesia, particularly in East Nusa Tenggara (NTT), where prevalence rates are among the highest nationally. This study aims to classify districts and municipalities in East Nusa Tenggara Province based on socioeconomic and health-related indicators associated with stunting vulnerability. Using the Gath–Geva (Fuzzy K-Means Entropy) clustering algorithm, four key variables were analyzed, including poverty rate, access to proper housing, open unemployment rate, and number of health facilities. The results identified three distinct clusters with different regional characteristics. Cluster 1 consists of areas with low poverty and well-developed health infrastructure but relatively high unemployment rates. Cluster 2 represents the most vulnerable regions characterized by high poverty, poor housing access, and limited health facilities, while Cluster 3 comprises more stable areas with better housing, low unemployment, and adequate healthcare services. The silhouette coefficient value of 0.41 indicates that the three-cluster structure provides a reasonably good level of separation and internal consistency. These findings highlight that stunting vulnerability is strongly influenced by socioeconomic disparities and the distribution of health infrastructure. Therefore, intervention strategies should be tailored to the characteristics of each cluster, emphasizing integrated actions in high-risk regions and preventive measures in more stable areas to accelerate stunting reduction across East Nusa Tenggara Province.