Classification of Child Stunting Status Using the K-Nearest Neighbor (KNN) Algorithm Based on Toddler Growth Data in East Lombok Regency, West Nusa Tenggara

Authors

  • Asno Azzawagama Firdaus Universitas Qamarul Huda Badaruddin Bagu
  • Arif Himawan Pusat Riset Masyarakat dan Budaya, Badan Riset dan Inovasi Nasional
  • Junaedi Department of Digital Business, Akademi Bisnis Lombok
  • Anggun Sindiana Universitas Qamarul Huda Badaruddin Bagu
  • Istianah Universitas Qamarul Huda Badaruddin Bagu
  • Baiq Selviana Pertiwi Universitas Qamarul Huda Badaruddin Bagu
  • Baiq Wangi Narsih Universitas Qamarul Huda Badaruddin Bagu
  • Kartika Yundia Universitas Qamarul Huda Badaruddin Bagu
  • Ahmad Azhari Universitas Ahmad Dahlan

DOI:

https://doi.org/10.37824/sij.v9i1.2026.1351

Keywords:

Stunting, K-Nearest Neighbor, Classification, Anthropometry, Under-fives

Abstract

Stunting is a major nutritional issue in Indonesia that significantly impacts children's physical growth, cognitive development, and future quality of life. Childhood stunting can be identified using nutritional status indicators—such as weight-for-age (W/A), height-for-age (H/A), and weight-for-height (W/H)—specifically by observing a Z-score below -2 for the H/A indicator. The growing volume of anthropometric data on children under five necessitates a rapid and objective method for identifying stunting status. This study aims to classify children's stunting status using the K-Nearest Neighbor (KNN) algorithm based on growth data from children under five in East Lombok Regency, West Nusa Tenggara. The dataset comprises records for 3,416 children, including information on gender, age, weight-for-age (W/A), weight-for-height (W/H), and height-for-age (H/A). Data preprocessing involved removing duplicates, handling missing values, transforming categorical data, and applying Min-Max normalization. The data was split into 70% training data and 30% testing data, with the KNN algorithm applied using k = 5. Model evaluation was conducted using a confusion matrix. The results demonstrate that the KNN model achieved an accuracy rate of 83.15%, indicating a strong capability to classify stunting status based on the children's anthropometric characteristics. These findings confirm that the KNN algorithm can serve as a tool for healthcare professionals to identify stunting more rapidly, objectively, and efficiently, thereby supporting early prevention and management efforts.

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Published

2026-07-31

Issue

Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026

Section

Articles

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Copyright (c) 2026 Asno Azzawagama Firdaus, Anggun Sindiana , Istianah, Baiq Selviana Pertiwi, Baiq Wangi Narsih , Kartika Yundia, Ahmad Azhari

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How to Cite

[1]
“Classification of Child Stunting Status Using the K-Nearest Neighbor (KNN) Algorithm Based on Toddler Growth Data in East Lombok Regency, West Nusa Tenggara”, SainsTech Innovation j., vol. 9, no. 1, pp. 643–648, Jul. 2026, doi: 10.37824/sij.v9i1.2026.1351.