Analisa Perbandingan Metode Klasifikasi Data Mining untuk Menentukan Tingkat Kemiskinan

Poverty is a social problem that requires proper data management and analysis to support decision-making. This study aims to classify the poverty status of the community into two categories, namely poor and non-poor, based on a socioeconomic dataset. The dataset used went through a data preprocessing stage that included data cleaning and attribute adjustment. The data processing process was carried out using classification techniques in data mining using a data processing application. Model evaluation was conducted to assess the classification capability based on the results of data testing. The results of the study indicate that the dataset used is able to support the process of classifying poverty status effectively. This research is expected to become the basis for the development of information systems that support decision-making in determining the poverty status of the community. Data Classification, Poverty Dataset, Poor and Non-Poor, Data Mining, Information Systems