Comparison of Ensemble Learning Method: Random Forest, Support Vector Machine, AdaBoost for Classification Human Development Index (HDI)

Classification in supervised learning is a way to find patterns data base that the classes are already known. In classification of machine learning, there term called ensemble classifier. The workings classifier aimed improve model accuracy and optimize performance. This study aims analyze comparison algorithms work with , including Random Forest, Support Vector Machine (SVM), AdaBoost. used Human Development Index ( HDI ) districts/cities Indonesia . O ther variables strongly related human development GRDP per capita, gross enrollment rate, n et labor force participation unemployment poverty depth, severity, average consumption capita. reason for using apart from being an important macroeconomic variable describing condition resources Indonesia, has obvious according Badan Pusat Statistik (BPS) so can be applied Comparison evaluation accuracy, specificity, sensitivity, kappa statistics analysis flow starts preprocessing resampling cross - validation then modeling AdaBoost algorithm T he final stage by comparing best models s districts/ cities HDI. results showed Forest had performance compared (SVM) value 85,23%, spe c ifi it y 71,63% sensitivit 95,05% coefficient 0,7698 From this research, developed help classify scores on Indonesia. Keywords: AdaBoost, Ensemble Learning,