An Application For Predicting Children’s Nutritional Status In The Free Nutritious Meals Programme Using Machine Learning

Abstract

The Free Nutritious Meal Program (Program Makan Bergizi Gratis - MBG) serves as a strategic initiative to enhance human resource quality from an early age through targeted nutritional intervention for school children. However, implementation efficiency at the school level remains hindered by conventional and manual monitoring of student nutritional status. This manual approach risks calculation errors in anthropometric indices and delays the early detection of nutritional issues. This study aims to design, develop, and evaluate the reliability of a web-based decision support system capable of automatically predicting elementary school students' nutritional status to support targeted distribution within the MBG program. The modeling technique utilizes the K-Nearest Neighbor (KNN) algorithm, processing four primary input variables: gender, age (months), weight (kg), and height (cm), in accordance with the Body Mass Index-for-Age (BMI-for-Age) anthropometric standards established by Permenkes RI No. 2 of 2020. The data preprocessing stage incorporates automatic unit conversion of height from centimeters to meters (Height_meters =  Height_cm / 100), Label Encoding, and Min-Max Scaling to normalize attribute scales and prevent variable dominance during Euclidean Distance calculation. Computational performance testing was conducted using a 4 x 4 Multi-class Confusion Matrix with a K=5 configuration on test samples from SDN 27 Bengkulu City. Experimental evaluation results demonstrate that the system achieved an Overall Accuracy of 90.91%, a Macro Average Precision of 96.88%, and a Macro Average Recall of 87.50%. These metrics prove that the KNN modeling approach possesses high precision and reliability in predicting four nutritional categories: thinness, normal, overweight, and obesity. In conclusion, the developed decision support application is scientifically validated and suitable for deployment as a digital ecosystem for schools and program managers to monitor students' physical health dynamics accurately, consistently, and in real time.