IMPLEMENTATION OF RANDOM FOREST AND SMOTE METHODS FOR ECONOMIC STATUS CLASSIFICATION IN CIREBON CITY

Abstract

The Indonesian government has tried various methods to eradicate poverty throughout the country, one of which is the fair distribution of social assistance to households based on their economic status classification. The determination of social assistance recipients can be influenced by political factors or personal relationships and can be misused, leading to assistance being given to individuals with specific political connections or support. This research aims to develop a household economic status classification system in Cirebon City using the Random Forest algorithm to address these issues. The data used in this research experienced an imbalance in the number of class instances, where the high-class instances were much fewer than the low and medium classes. To address this, the Synthetic Minority Oversampling Technique (SMOTE) was employed. In this study, various testing scenarios were conducted to obtain the best model for accurately predicting household economic status. Based on the research results, the best testing was achieved using Random Forest and SMOTE with a random state of 0, obtaining an accuracy of 93% and excellent performance in classifying each class. When testing unlabeled data, Random Forest successfully predicted 24 out of 30 actual data, resulting in an accuracy rate of 80%. Although this accuracy is lower than what was achieved by Random Forest and SMOTE with a random state of 0, it can be said that the application successfully classifies household economic status in Cirebon City effectively.