Child Stunting Risk Analysis through Machine Learning Models using XGBoost Algorithm
Abstract
Stunting is a chronic nutritional disorder that significantly affects child growth, development, and the overall quality of future human resources. According to the 2024 Indonesian Nutritional Status Survey (SSGI), the prevalence of stunting remains high at 19.8%, equivalent to approximately 4.48 million children under five. Early detection of stunting risk is essential for timely and data-driven interventions. This study employed the CRISP-DM methodology, encompassing business understanding, data collection, preparation, modeling, and evaluation phases. The dataset was processed through cleaning, variable encoding, and stunting status classification based on WHO standards. An XGBoost-based predictive model was developed and evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved 98% accuracy in predicting stunting risk. Feature importance analysis revealed that height is the most influential variable determining stunting risk.
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