Implementasi XGBoost dan Random Forest pada Penyakit Stunting

Stunting is a serious problem in Indonesia, affecting around 21.6% of children under five due to chronic malnutrition. This research focuses on implementing the XGBoost and Random Forest methods to identify risk factors for stunting. The XGBoost method achieved 90% accuracy, while Random Forest achieved 85%. These results show that XGBoost has better performance in classifying stunting risk. This research is important for the development of more effective prevention strategies for stunting, considering nutritional, economic and environmental factors.