KLASIFIKASI KEMISKINAN DI PROVINSI BENGKULU MENGGUNAKAN METODE POHON KLASIFIKASI GABUNGAN

Abstract

Poverty is a global problem that is of concern to the world. It can be seen from the SDGs declaration, which makes poverty a top priority. Good poverty management will help solve other world problems such as hunger, health, welfare, education, and sanitation. To achieve the goal of handling poverty quickly and maximally, an analysis that can identify poor households correctly can be designed so that a targeted program can be designed according to the characteristics of households classified as poor households. One of the statistical methods used to see these characteristics is a classification tree such as the Classification and Regression Tree (CART). The weakness of the cart method if there is an unbalanced dataset type can be overcome by the SMOTE method. In addition to the CART method, classification will be carried out using Random Forest and Xgboost. The results show that the random forest CART model has the highest AUC value in balanced data. It is indicated that this method is better than the others. Based on random forest, variables that determine the most determined poor households are number of household members, last diploma of the head of the home, and floor area of the house.