Binary Logistic Regression and Support Vector Machine for Classifying the Human Development Index

Abstract

Binary Logistic Regression and Support Vector Machine (SVM) are two widely used classification methods in data analysis, especially for problems with categorical target variables. In this study, these two methods are compared to classify the Human Development Index (HDI) status of Indonesia in 2024. The initial data consists of five predictor variables, but after conducting a correlation analysis to avoid multicollinearity, only three variables were used in the modeling. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to address class imbalance. Binary Logistic Regression was chosen due to its good interpretability, while SVM was used as a comparison due to its robustness against outliers. Evaluation results show that Binary Logistic Regression achieved an accuracy of 87.85%, slightly higher than SVM, which reached 86.92%. Therefore, Binary Logistic Regression is considered more optimal in classifying HDI status on the data that has been balanced and simplified. This study contributes to the application of statistical methods and machine learning in supporting human development analysis based on data.