Clustering Status Gizi Menggunakan Algoritma K-Means Dengan Pendekatan CRISP-DM

Abstract

The nutritional status of toddlers is a key indicator for assessing public health levels. Nutritional problems such as undernutrition remain common and require effective analysis to identify data patterns quickly and accurately. This study aims to cluster the nutritional status of toddlers in the Bukuan Community Health Center (Puskesmas) Posyandu area using the K-Means algorithm with the CRISP-DM approach. The main challenge in this study is that the processing of nutritional status data is still done manually, making it less effective in quickly and accurately identifying patterns and risk groups. The dataset consists of 2,145 records of toddlers from 12 Posyandu, with primary attributes including weight, height, age, and gender. The research process was conducted through the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, and evaluation, without deployment implementation since the study focused on data analysis. The clustering process was performed using the K-Means algorithm, with the optimal number of clusters determined via the Elbow method, resulting in three clusters. Model evaluation using the Silhouette Score yielded a value of 0.629, indicating that the clustering quality falls into the “good” category. The results show that data on toddlers can be grouped into three nutritional status categories: under-nutrition, adequate nutrition (normal), and over-nutrition, based on centroid values. The data distribution indicates that the adequate nutrition category dominates, though there remains a significant number of cases in the under-nutrition category. Thus, the application of the K-Means algorithm provides more structured and accurate information for identifying the nutritional status of toddlers and can serve as a basis for data-driven decision-making in public health programs.