<![CDATA[Stunting is a chronic health issue that significantly impacts the physical growth and cognitive development of children, particularly in developing countries such as Indonesia. This study aims to classify stunting status among toddlers in the Singgahan District by applying the Support Vector Machine (SVM) algorithm, optimized using Grid Search Cross-Validation. The dataset consists of 642 toddler records with nine attributes representing nutritional and growth conditions. The classification process involves several stages, including data preprocessing, handling data imbalance using the SMOTE method, and model performance evaluation through 5-fold cross-validation. The results show that the SVM algorithm without optimization achieved an accuracy of 69.83%, while optimization with Grid Search Cross-Validation significantly increased the accuracy to 93.33%. These findings indicate that the application of SVM with hyperparameter tuning via Grid Search Cross-Validation is effective in improving classification accuracy for stunting status in toddlers. This research contributes to the use of machine learning in supporting decision-making processes in public health sectors.]]>
Copyrights © 2025
