Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting

Stunting has been widely known as the highest case of malnutrition suffered by toddlers in world and a bad impact on children's future. In 2018, Indonesia was ranked 31st stunting 4th Southeast Asia. About 30.8% (roughly 3 out 10) children under 5 years suffer from Indonesia. To support government policy making handling stunting, it is undoubtedly necessary to classify levels regions this work, hierarchical agglomerative non-hierarchical clustering compared evaluated perform data. The cluster uses Single Linkage, Average Complete Ward Method, while K-Means, K-Medoids (PAM) Clustering, Fuzzy C-Means. This study data 12 IKPS indicators 34 provinces 2018. Based results evaluation using Connectivity Coefficient, Dunn Index, Silhouette Davies Bouldin Xie & Beni Calinski-Harabasz show that Linkage best method with optimal number clusters four clusters. first good level management which consists 28 provinces. second only one province, DI Yogyakarta very handling. third poor rates. Finally, last consisting Papua,