Evaluation of distance measurement techniques in the k-NN method for toddler nutritional status classification

Authors

  • Rahmatina Hidayati Universitas Merdeka Malang
  • Anita Universitas Merdeka Malang
  • Sri Lestanti Universitas Islam Balitar

DOI:

https://doi.org/10.26905/jisad.v3i1.15393

Keywords:

toddler, classification, k-Nearest Neighbors, nutritional status

Abstract

Toddler nutritional status is an essential indicator in assessing public welfare and health. At Rongga Koe Village Health Post, determining nutritional status is still done manually, so it takes a long time and is prone to errors. This study aims to develop a classification system for toddler nutritional status using the K-Nearest Neighbors (k-NN). The data used was 100 samples with five parameters: gender, age, weight, height, and upper arm circumference. The classification process was carried out with variations in the ratio of training and testing data (90:10, 80:20, 70:30, 60:40), as well as the k value and distance calculation method (Euclidean, Manhattan, Chebyshev, Mahalanobis). The results showed that the best combination was obtained at a ratio of 90:10 and a k value = 9 with the Mahalanobis Distance method, which achieved the highest accuracy of 85.7%. This study proves that the K-NN method is effective in helping to classify nutritional status digitally and more efficiently.

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Published

2025-04-21

Issue

Vol. 3 No. 1 (2025): March 2025

Section

Articles

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