Implementasi Data Mining Untuk Klustering Stunting Gizi Pada Balita Dipuskesmas Sigambal Meggunakan Metode K-Medoids Dan K-Means - Melisa Melisa; Syaiful Zuhri Harahap; Masrizal Masrizal (Jurnal Informatika, 2024)
The aim of this study was to identify and understand the different characteristics of toddlers in the context of factors that contribute to nutritional stunting. By using the clustering method, this study aims to group toddlers into several groups based on the similarity of their characteristics, so that more targeted interventions can be designed in dealing with stunting problems. Through this approach, it is hoped that significant patterns and risk factors can be found that distinguish stunted toddlers from toddlers who grow normally, and provide insights that can be used by policy makers and health practitioners to improve the quality of life of children. The method used in this study involves the application of two clustering techniques, namely K-Means and K-Medoids to Group sample data of 116 toddlers. The clustering process is carried out by measuring the distance between the toddler data and the centroid or medoid to determine which group is most suitable. The Data were analyzed to find patterns identifying unique characteristics of each cluster, reflecting differences in nutritional stunting-related risk factors.This process helps in differentiating groups of toddlers who are prone to stunting from those who are not, so that the analysis can be focused on the groups most in need of intervention. The results of clustering analysis showed that as many as 48 toddlers entered the C1 cluster, while the other 68 toddlers entered the C2 cluster. Each cluster describes two groups of toddlers with different characteristics in the context of nutritional stunting risk factors. The findings provide deep insight into the significant differences between the two groups, allowing researchers to identify specific patterns and risk factors. This information is then used to design more specific and effective interventions in addressing nutritional stunting in toddlers, taking into account the unique characteristics of each cluster that has been identified.