K-Means Clustering with Elbow Method for Stunting Risk Detection in Toddlers Using Anthropometric and Nutritional Data

Stunting remains a critical public health challenge in Indonesia, primarily due to inadequate nutrition and recurrent infections in early childhood. This study aimed to identify patterns of stunting risk by integrating anthropometric and dietary data, specifically sugar consumption, using an unsupervised machine learning approach. A total of 20 toddlers aged 12-59 months from Purwokerto Selatan participated. Anthropometric data (age, weight, height) and dietary intake (sugar consumption, snack frequency) were collected via a caregiver questionnaire. K-Means clustering was applied, with the optimal number of clusters determined using the Elbow Method (K=2). Two clusters were identified: Cluster 0, with a lower risk of stunting, and Cluster 1, with a higher proportion of toddlers at risk. Cross-tabulation with stunting status validated this, showing that Cluster 1 contained more children with "Potential" stunting. Internal validation using the Silhouette score (0.252) and PCA visualization confirmed the clustering's robustness. This study demonstrates the potential of combining anthropometric and dietary data for stunting risk profiling, suggesting a complementary approach for growth monitoring programs and targeted interventions.