Analisis Perbandingan Algoritma SVM, Random Forest dan Logistic Regression untuk Prediksi Stunting Balita

Abstract

The prevalence of stunting in Banjarmasin City in 2023 reached 26.5%, exceeding the WHO target (below 20%). Stunting impacts physical growth, cognitive development, and long-term economic productivity. The purpose of this study is to compare the performance of SVM, random forest, and logistic regression algorithms in classifying the stunting status of toddlers. The approach we use is comparative quantitative with machine learning methods for health data classification. Data totaling 2,231 under-five records were obtained from the Banjarmasin City Health Office. We used age, weight, height, and z-score information. Data preprocessing includes handling missing values, categorical data transformation, numerical data standardization, and feature selection. The dataset was divided into 70:30 and 80:20 ratios using stratified sampling with 5-fold cross-validation. Our results show that SVM is the best model, with accuracy 92%, precision 91%, recall 99%, F1-score 95%, and AUC 99%, followed by random forest (accuracy 91%, AUC 98%) and logistic regression (accuracy 92%, AUC 97%). SVM showed superior performance due to its ability to find the optimal hyperplane that maximally separates stunted and non-stunted classes, as well as its effectiveness in handling non-linear data through kernel tricks. SVM's good generalization ability on new data makes it a top choice as a predictive tool for stunting prevention in Banjarmasin City.