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Nicea Roona Paranoan
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Decision Sciences, Operations Research & Management Mathematics

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Analysis of Stunting Data in Indonesia Using K-Means and Self Organizing Map (SOM) Allo, Caecilia Bintang Girik; Nicea Roona Paranoan; Winda Ade Fitriya B; Bobi Frans Kuddi; Feby Seru
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.7778

Abstract

Abstract. Stunting is a global public health concern, including in Indonesia. The Indonesian government establishes a target for stunting prevalence reduction every year. The government is aiming for a stunting prevalence of 18% in 2025. The government certainly requires policy recommendations to achieve this target. Clustering analysis can be used to identify provinces with similar characteristics or those that still require special attention based on stunting related indicators. There are several clustering methods, including K-Means and Self-Organizing Map (SOM). This study aims to classify provinces in Indonesia based on indicators related to stunting and to compare the performance of two clustering methods. Based on the obtained data, it was found that the data contains outliers. The best clustering method can be determined using the Silhouette Coefficient (SC) and Davies Bouldin Index (DBI). The results showed that the highest SC value, 0.62, was obtained using the SOM method and the lowest DBI, 0.75, was obtained also using SOM method. Two clusters were formed using the SOM method. Cluster 1 consisted of 36 provinces in Indonesia. Cluster 2 consisted of 2 provinces, namely Highland Papua and Central Papua.
Co-Authors Allo, Caecilia Bintang Girik Bobi Frans Kuddi FEBY SERU Winda Ade Fitriya B