Segmentasi Status Gizi Balita Berdasarkan Umur, Berat, dan Tinggi Menggunakan K-Means Clustering
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Abstract
The nutritional status of toddlers is an essential indicator for monitoring their growth and development. This study aims to classify toddlers based on similarities in physical attributes using the K-Means Clustering algorithm. A total of 100 toddler records containing age, weight, height, and BMI were processed in RapidMiner through attribute selection, preprocessing, and cluster formation. Several clustering scenarios were tested, and the four-cluster configuration produced the most representative segmentation. The results show distinct characteristics across clusters. Cluster 0 represents toddlers with relatively larger physical development, with centroid values of 33.11 months (age), 10.13 kg (weight), 0.723 m (height), and BMI 26.43. Cluster 1 contains the oldest toddlers but with lower weight and BMI (8.85 kg; 14.37), indicating potential undernutrition. Cluster 2 has the youngest average age (12.5 months) but relatively higher weight and height, while Cluster 3 groups younger toddlers with smaller body size. These findings demonstrate that K-Means provides structured nutritional segmentation, supporting health workers in identifying groups requiring targeted nutritional interventions.
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