Stunting Classification Model For Toddlers Using SMOTE and Support Vector Machine (SVM) (Case Study: Samalanga Community Health Center)

Abstract

Stunting is a growth disorder that has long-term impacts on child development. This study aims to develop a classification model for determining stunting status in toddlers using the Support Vector Machine (SVM) algorithm, with a case study conducted at the Samalanga Community Health Center. The dataset used consists of 1,205 toddlers. The research stages include preprocessing, data balancing using SMOTE, and parameter tuning using GridSearchCV. The developed model successfully achieved an accuracy of 0.97, an ROC-AUC of 0.96, and an average f1-score of 0.97. These results indicate that the model can accurately distinguish between stunted and non-stunted toddlers. Benchmarking against public datasets shows that the model in this study has a 2% higher accuracy and a 4.7% higher ROC-AUC value compared to previous studies. These findings indicate that the applied pipeline approach is effective in improving classification accuracy. The resulting model has the potential to support fast and accurate stunting classification.