Enhanced Possibilistic Fuzzy C-Means Clustering Stunting Prevalence in Indonesia

Abstract

Stunting in toddlers is a significant public health problem in Indonesia due to its potential to inhibit child development and cause long-term adverse effects. Clustering the prevalence of stunting provides valuable insights for designing effective prevention policies. This study employs the Possibilistic Fuzzy C-Means (PFCM) method, validated using the Modified Partition Coefficient (MPC) index, to cluster stunting prevalence in Indonesia. The PFCM method integrates Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM), balancing membership degrees with probabilistic measures. The primary advantages of this method are its capability to handle data with uncertain membership degrees, robustness against noise, and flexibility in defining probabilistic membership values. The results obtained show that clusters with high stunting prevalence are dominated by nine provinces, namely Aceh, Jambi, Bengkulu, Bangka Islands, Central Kalimantan, Central Sulawesi, Gorontalo, West Papua, and Papua. The MPC validity score of 0.704 confirms the effectiveness of the PFCM method in categorizing stunting prevalence well, making it a robust tool to support policymaking in stunting prevention efforts.