Performance Comparison of Child Stunting Prediction Support Vector Machine vs Random Forest with Grid Search Optimization

Abstract

Stunting is a serious global health problem, particularly in developing countries. Its prevalence is high in Indonesia, reaching approximately 24.4% among children under five in 2021. This condition, defined as failure to thrive due to chronic malnutrition, repeated infections, and a lack of psychosocial stimulation, has long-term impacts on an individual's cognitive development and productive capacity. This study aims to conduct a comparative analysis of the Support Vector Machine and Random Forest algorithms in predicting stunting in children, with a focus on evaluating the impact of hyperparameter optimization using Grid Search on model performance. This study used the public stunting dataset from Kaggle and included data preprocessing steps such as handling missing values, duplication, encoding, and scaling. The data was then divided into 80% for training, 10% for testing, and 10% for validation. Comprehensive evaluation metrics such as precision, recall, F1-score, and ROC-AUC were also used to assess model performance.  Grid Search optimization was applied to both models to find the best hyperparameter combination. Experimental results showed that Grid Search optimization significantly improved the accuracy of the SVM model from 94.29% to 98.37%. Meanwhile, the Random Forest model demonstrated very high performance, achieving 99.59% accuracy both before and after Grid Search optimization. These findings underscore the significant potential of machine learning models in supporting stunting prevention efforts for public health intervention policies. This research contributes to the development of machine learning-based decision support systems for public health, particularly in early detection and intervention strategies for stunting.