Application of Ensemble Bagging Support Vector Machine for Early Detection of Childhood Stunting

Abstract

Stunting is a significant public health issue in Indonesia, characterized by a child's height being below the age standard. Maternal knowledge and family economic level are key factors influencing children's nutritional status, thus requiring accurate classification methods for early stunting risk detection. This study aims to develop a machine learning-based classification model for stunting risk using Support Vector Machine (SVM) with a quadratic polynomial kernel and evaluate its performance improvement through the ensemble Bagging SVM approach. Primary data were collected from 100 mothers of children under five, using a five-point Likert scale questionnaire to assess maternal knowledge (X₁) and family economic level (X₂). The SVM model was constructed using a quadratic polynomial kernel and compared to Bagging SVM, which applies bootstrap resampling and majority voting. Model performance was evaluated using accuracy, sensitivity, and specificity. The basic SVM model yielded 85% accuracy, 90% sensitivity, and 80% specificity. The SVM Bagging approach showed performance improvements, with 95% accuracy, 100% sensitivity, and 94% specificity. These results indicate that SVM Bagging reduces misclassification. The SVM Bagging approach was more effective than a single SVM in classifying stunting risk. The novelty and scientific contribution of this study lie in applying ensemble machine learning methods, particularly Bagging SVM, to enhance early detection of stunting risk. This method offers a reliable solution for improving stunting risk classification accuracy and strengthening targeted nutrition interventions in Indonesia.