Clustering Analysis Of Toddler Nutritional Status Using The K-Means Method On Posyandu Data

Yurizka Sri Nanda; Nurul Rahmadani; Ahmad Muhazir

doi:10.30865/jurikom.v12i4.8947

Authors

Yurizka Sri Nanda Universitas Royal Asahan Sumatera Utara
Nurul Rahmadani Universitas Royal Asahan Sumatera Utara
Ahmad Muhazir Universitas Royal Asahan Sumatera Utara

DOI:

https://doi.org/10.30865/jurikom.v12i4.8947

Keywords:

Data Mining, K-Means, Clustering, Stunting

Abstract

The issue of toddler nutritional status remains a serious concern because it can affect children's health and development, including the risk of stunting and cognitive impairment. At the Tanjung Asri Village Health Center, nutritional status is still recorded manually, which is inefficient and prone to classification errors. This study aims to develop a system for classifying the nutritional status of infants using the K-Means Clustering method based on desktop software to simplify the classification of nutritional status into three categories: malnourished, moderately nourished, and well-nourished. This study uses a quantitative approach with primary data from 100 infants collected through observation and interviews in May and June 2025. The clustering process was performed using RapidMiner with the parameter k = 3. The test results showed that the K-Means method was able to produce accurate centroid centers consistent with manual results. In May 2025, there were 22 infants with poor nutrition, 21 infants with moderate nutrition, and 7 infants with good nutrition, while in June 2025, there were 27 infants with poor nutrition, 8 infants with moderate nutrition, and 15 infants with good nutrition. The developed system has proven effective in supporting the classification and monitoring of infant nutritional status in a more objective and efficient manner.

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