Implementation of CART Method with SMOTE for Household Poverty Classification in Mentawai Islands 2023

Abstract

Poverty is a condition in which individuals or groups are unable to fulfill their basic needs due to economic pressure or limited resources. The Classification and Regression Trees (CART) method is a classification technique in the form of a classification tree, which describes the relationship between independent and dependent variables. Data imbalance can lead to low sensitivity values and area under curve (AUC) values. One method that can overcome unbalanced data is to perform Synthetic Minority Oversampling Technique (SMOTE). SMOTE is a technique with the addition of artificial data in the minority class at a stage before analyzing the data. The purpose of this research is to compare the model without and with SMOTE in CART method. The use of SMOTE is applied to balance the amount of data on each poor household. The accuracy value of the method without SMOTE is 89% while with the SMOTE method is 79%. However, the sensitivity value has increased by 80%. Meanwhile, the AUC value in the CART method with SMOTE increased by 31%. So in this study it can be concluded that CART classification analysis with SMOTE is able to provide better performance compared to CART classification analysis without SMOTE.