Segmentation of Toddlers Based on Nutritional Status Using Agglomerative Hierarchical Clustering with Average Linkage

Abdul Malid; Sriani

doi:10.30865/json.v7i3.9598

Authors

Abdul Malid Universitas Islam Negeri Sumatera Utara
Sriani Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.30865/json.v7i3.9598

Keywords:

Agglomerative Hierarchical Clustering, Average Linkage, Cluster-Based Nutrition Intervention, Segmentation, Toddler Nutritional Status

Abstract

Nutritional status among children under five remains an important public health concern, particularly in developing regions where early detection of growth problems is essential for effective intervention. Conventional nutritional assessments often rely on categorical classifications that may not fully capture variations in anthropometric characteristics among toddlers. This study aims to segment children under five based on nutritional status using the Agglomerative Hierarchical Clustering (AHC) algorithm with the Average Linkage method in the NA-IX-X District, North Labuhanbatu Regency. The study used secondary anthropometric data from 1,452 children obtained from the Aek Kota Batu Public Health Center. Quantitative variables, including body weight, height, and age, were standardized using z-score transformation prior to clustering analysis. The results show that a three-cluster configuration provides the optimal segmentation, with a Silhouette Coefficient value of 0.5154, indicating a moderate clustering structure. Cluster 1 (n = 180) shows relatively lower anthropometric measurements with an average body weight of 7.3 kg and height of 68.3 cm. Cluster 2 (n = 511) represents intermediate measurements with an average body weight of 11.5 kg and height of 87.8 cm, while Cluster 3 (n = 761) reflects higher measurements with an average body weight of 15.0 kg and height of 101.7 cm. Dendrogram analysis indicates that a cutting point at height = 1.5 produces the most interpretable cluster separation. These findings demonstrate that hierarchical clustering can support more targeted nutritional intervention strategies at the community health center level.

References

D. R. R. Putri, N. Ulinnuha, and P. K. Intan, “Comparison of linkage methods in hierarchical clustering for grouping districts/cities in East Java based on stunting determinants,” Journal of Applied Informatics and Computing, 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

S. L. Munira, “Hasil Survei Status Gizi Indonesia (SSGI) 2022,” Badan Kebijakan Pembangunan Kesehatan, Jakarta, Indonesia, 2023.

M. Ishanifa, “Use of hierarchical clustering method with complexity invariant distance on provincial rice prices in Indonesia,” Journal of Applied Statistics and Data Science, vol. 2, no. 1, pp. 45–57, Mar. 2025, doi:10.21776/ub.jasds.2025.002.01.5.

I. A. Putrinugroho, M. Anshori, and W. T. Kusuma, “Linkage comparison in agglomerative hierarchical clustering for clustering students’ knowledge of first aid for stroke emergencies,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 2, pp. 937–944, May 2025, doi: 10.31004/riggs.v4i2.564.

“Data mining and visualization for toddler nutrition monitoring in community health centers,” Journal of System and Management Sciences, Jun. 2024, doi: 10.33168/jsms.2024.0703.

O. Ardhiyanto, M. S. Asyidqi, A. Y. P. Yusuf, and T. A. Munandar, “Clustering of child nutrition status using hierarchical agglomerative clustering algorithm in Bekasi City,” Jurnal Penelitian dan Pengabdian Masyarakat, 2023.

H. Oktavianto et al., “Clustering analysis for regional segmentation,” Bina Insani ICT Journal, vol. 10, no. 2, pp. 145–153.

A. Setiawan, A. Waladi, and R. Ashar, “Hotspot Clustering in Bangka Belitung Islands Province Using Agglomerative Hierarchical Clustering Algorithm,” 2025. [Online]. Available: http://www.mase.or.id

A. C. Sembiring and I. Nurwati, “Analisis Faktor Determinan Status Gizi Balita: A Systematic Review Analysis Of Determinat Factors Of Nutritional Status Of Toddler: A Systematic Review,” Jurnal Kesehatan Holistic, vol. 08, 2024, doi: 10.33377/jkh.v8i2.19.

O. Purwaningrum, Y. Y. Putra, and A. A. Arifiyanti, “Determining toddler nutritional status groups using the K-means method,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 15, no. 2, 2021.

S. Wulandari, “Clustering Indonesian provinces based on prevalence of stunting toddlers using agglomerative hierarchical clustering,” Faktor Exacta, vol. 16, no. 2, Jul. 2023, doi: 10.30998/faktorexacta.v16i2.17186.

N. D. Wahab, S. K. Nasib, Nurwan, D. Wungguli, and N. I. Yahya, “Clustering of stunting data in Indonesia using X-means and agglomerative hierarchical clustering methods,” Research in the Mathematical and Natural Sciences, vol. 4, no. 1, pp. 52–64, Feb. 2025, doi: 10.55657/rmns.v4i1.201.

B. W. Otok, Purhadi, R. Sriningsih, and D. S. Dila, “Segmentation of toddler nutritional status using REBUS and FIMIX partial least square in Southeast Sulawesi,” MethodsX, vol. 12, Jun. 2024, doi: 10.1016/j.mex.2023.102515.

B. Teshome, W. Kogi-Makau, Z. Getahun, and G. Taye, “Magnitude and determinants of stunting in children under five years of age in food surplus region of Ethiopia: The case of West Gojam Zone,” Ethiopian Journal of Health Development, vol. 23, no. 2, Mar. 2010, doi: 10.4314/ejhd.v23i2.53223.

C. Tjipta et al., “Comparison of K-means++ and agglomerative hierarchical methods in clustering healthcare workers,” vol. 10, no. 2, 2025.

Y. B. Roza, S. Defit, and S. Arlis, “Cluster analysis using the K-means algorithm for grouping toddler nutritional conditions at Posyandu,” Bulletin of Computer Science Research, vol. 5, no. 5, pp. 1182–1187, Aug. 2025, doi: 10.47065/bulletincsr.v5i5.752..

A. Rahmah, N. F. Khusna, S. A. Sanmas, S. Aulia, S. Amaria, and F. Fauzi, “Comparison analysis of hierarchical clustering methods,” 2025.

J. Penelitian dan Pengabdian Masyarakat, O. Ardhiyanto, M. Salam Asyidqi, A. Yunizar Pratama Yusuf, and T. Ai Munandar, “Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City,” 2023.