Analisis Klasterisasi K-Means untuk Klasifikasi Status Gizi dan Kondisi Bayi Baru Lahir Berdasarkan Kecamatan di Kabupaten Probolinggo 2023

Abstract

This study analyzes the nutritional status and condition of newborns in Probolinggo Regency in 2023 using the K-Means clustering method. Data from 24 sub-districts were obtained from the Central Statistics Agency (BPS) and include variables such as the number of babies born, cases of low birth weight (LBW), and malnourished babies. The research stages include data cleaning, normalization, determining the optimal number of clusters using the Elbow Method, implementing the K-Means algorithm, evaluating the results using Silhouette Analysis, and visualizing data using scatter plots and Principal Component Analysis (PCA). The results of the analysis produced five clusters that reflect different characteristics related to infant nutrition problems. As much as 79.7% of data variation can be explained by differences between clusters. This study recommends data-based policies such as nutrition education, increasing access to nutritious food, routine nutritional monitoring, and spatial mapping for more appropriate interventions. The results are expected to support maternal and child health policies and address the Triple Burden of Malnutrition in Indonesia.