CLUSTERING OF TODDLER DATA AT PUSKESMAS NANGGALO USING THE K-MEANS ALGORITHM

Abstract

Indonesia is still facing a double nutritional problem in children under five, namely malnutrition and overnutrition, which often occurs in children aged 0-59 months, an important phase in physical and mental development. This study aims to cluster data on the nutritional status of toddlers at the Nanggalo Health Center, Nanggalo District, Padang City, using clustering methods in the data mining process. Using the K-Means algorithm, toddler data is clustered based on body weight (BW) and height (TB) indicators. The Nanggalo Community Health Center was chosen as the research site because the available nutrition data is complete and representative, thus providing a comprehensive picture of the nutritional status of children under five in the area. The clustering results in three main categories: underweight, normal and overweight, which makes it easier to identify groups of under-fives who need more attention. This information is expected to be accessible to parents and health workers to support better decision-making on under-five nutrition management. With this approach, this study is expected to contribute to improving the quality of nutrition services at the Nanggalo Health Center and support the creation of a healthy generation that is accustomed to a healthy lifestyle