COMPARISON OF MACHINE LEARNING METHODS IN CLASSIFYING POVERTY IN INDONESIA IN 2018

Abstract

Poverty is still one of the main problems in economic development besides inequality, unemployment, and economic growth. This study aims to model poverty directly using a discrete choice model, namely the machine learning classification method. The data used are imbalanced data where one of the categories is small enough so that the resample of both sampling method is used. In this study, several machine learning methods were applied, including the Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Rotation Forest. The results show that the technique of using resample both samplings provides optimal results for the four machine learning methods. If viewed from the indicators of accuracy, specificity, sensitivity, AUC, and the highest Kappa coefficient produced, the best method is the KNN method. The KNN model has an accuracy value of 0.73 percent, sensitivity of 0.68 percent, specificity of 78 percent, and AUC of 0.73.