Klasifikasi Penentuan Tingkat Kesejahteraan Keluarga Menggunakan Metode Naive Bayes Pada Kelurahan Pematang Kandis

Abstract

Family welfare is a crucial factor in determining the social and economic development of a region. This study aims to classify the level of family welfare in Pematang Kandis Subdistrict using machine learning model of the Naïve Bayes method, a probability-based algorithm that is effective in data classification. The data used in this study were obtained through surveys covering various economic, social, and demographic factors, such as income, education level, type of occupation, housing conditions, and the number of dependents. The research process includes data collection, preprocessing, training the Naïve Bayes model, and evaluating the model's performance using accuracy, precision, recall, and F1-score metrics. The results indicate that the Naïve Bayes method achieves a high level of accuracy in classifying family welfare levels. Therefore, this method can serve as a supporting tool in formulating social policies by local governments. Through this approach, the government is expected to identify families in need of assistance and design more targeted welfare programs.