Klasifikasi Risiko Stunting 1000 HPK Menggunakan Algoritma K-Means di Aia Gadang Timur

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Published: 31-01-2026
DOI: https://doi.org/10.65258/jutekom.v2.i1.51
Keywords:
K-Means Clustering, Sistem Informasi, 1000 HPK, Data Mining, Public Health

Main Article Content

Mila Yudia
ITS Khatulistiwa
Primadela Antari
ITS Khatulistiwa
Yuli Hartati
ITS Khatulistiwa
Fanny Septiani Bufra
ITS Khatulistiwa

Abstract

Stunting is a public health problem that impacts child growth and development, particularly during the First 1,000 Days of Life (1000 HPK). This study aims to apply the K-Means Clustering algorithm to classify stunting risk levels based on 1000 HPK data in Nagari Aia Gadang Timur. The data used include maternal and child health indicators, such as maternal nutritional status, prenatal check-up history, birth weight, exclusive breastfeeding, and child growth measurements. The K-Means algorithm was used to group the data into several clusters. The results showed that the formed clusters were able to clearly distinguish low, medium, and high stunting risk groups. The application of K-Means Clustering can facilitate early identification of stunting risk and support data-driven decision-making in planning stunting prevention and intervention programs at the nagari level.

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Vol. 2 No. 1 (2026): Januari 2026

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Articles
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite

Klasifikasi Risiko Stunting 1000 HPK Menggunakan Algoritma K-Means di Aia Gadang Timur (M. Yudia, P. Antari, Y. Hartati, & F. S. Bufra , Trans.). (2026). Jurnal Teknologi Informasi Dan Ilmu Komputer, 2(1), 47-50. https://doi.org/10.65258/jutekom.v2.i1.51

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