KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN DATA ANTROPOMETRI DENGAN ALGORITMA K-MEANS CLUSTERING - Nurul Natasya; Bambang Irwansyah; Muhammad Azwar Al Ayyub (Smart Education, 2026)
Nutritional status is an important indicator for assessing the growth and development of toddlers. Posyandu Desa Simpang Dolok has anthropometric data of toddlers, including age, weight, height, and head circumference; however, the data has not been optimally utilized to identify patterns in nutritional status grouping. This study aims to implement the K-Means Clustering algorithm to classify the nutritional status of toddlers, design a web-based application for the classification process, and determine the resulting clusters. The study used 200 toddler records, which were normalized using the Min-Max Normalization method before the clustering process. The system was developed using PHP and MySQL. The results showed that the K-Means algorithm successfully grouped the data into three clusters: 58 toddlers (29%) in the undernutrition cluster, 85 toddlers (43%) in the normal nutrition cluster, and 57 toddlers (28%) in the overnutrition cluster. In addition, the developed application was able to assist Posyandu officers in processing data and obtaining information on toddlers’ nutritional status more quickly, effectively, and systematically. Therefore, the implementation of the K-Means algorithm can be used as an alternative approach to support the monitoring of toddlers’ nutritional status at Posyandu Desa Simpang Dolok.