Analisis Klaster K-Means Dan Agglomerative Nesting Pada Indikator Stunting Balita Di Indonesia - Rosi Anisya Faujia; Eni Sawitri Setianingsih; Hasih Pratiwi (Politeknik Statistika STIS, 2022)
The second target of the second goal of the SDGs is to eliminate all forms of malnutrition, one of which is an indicator of the prevalence of stunting in children under five. This study aims to classify and identify the characteristics of provinces in Indonesia based on stunting indicators in children under five. The method used is the k-means algorithm and agglomerative nesting (AGNES) clustering. By comparing the average silhouette value, it can be seen that the hierarchical clustering method of the AGNES algorithm with single linkage has the highest average silhouette value of 0,67 which is a strong cluster. Based on the results of the analysis obtained 2 optimum clusters. The characteristics of cluster 2, namely Papua Province, are indicators of a high incidence of stunting, because in this cluster immunization, access to sanitation, access to health facilities, high school education levels are low and LBW <2.500 grams. Meanwhile, the other 33 provinces are in cluster 1 with indicators of immunization, access to sanitation, access to health facilities, high school education and LBW <2.500 grams so that this cluster is included in the low incidence of stunting indicators.