Spatial Analysis and Machine Learning Integration for Nutritional Status Mapping Using ANN and Random Forest Models - Anggraini, Desi Anis; Kurniawan, Fachrul; Nugroho, Fresy; Koeshardianto, Meidya; Iqbal Bachtiar, Mohammad (Knowledge Engineering and Data Science, 2025)
Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify high-risk zones. The integration of machine learning and spatial analysis proved effective in enhancing classification accuracy, improving data interpretation, and supporting data-driven nutritional policy and regional health decision-making.