Socio-Economic Status Classification of Neighborhood Residents Using the Decision Tree Algorithm

Abstract

This study aims to analyze the socioeconomic classification of RT residents using the Decision Tree algorithm. The analysis was carried out using data that includes attributes such as the number of family members, education, and occupation. The results of the study show that the Decision Tree algorithm is capable of producing a clear and structured classification model, with the number of family members being the dominant factor in class distribution. Most residents were classified into the Middle socioeconomic category (68.3%), followed by the Low category (26.8%), and the High category (4.9%). These results reflect that the majority of residents have relatively stable socioeconomic conditions, although there are still groups that require special attention. This classification model provides important insights for policymakers to design more focused assistance and economic empowerment programs. This study also recommends further development by adding more diverse attributes and comparing the Decision Tree algorithm with other classification methods to improve the model’s accuracy and validity.