Klasifikasi Naïve Bayes Pada Data Status Kesejahteraan Rumah Tangga Penerima Manfaat di Kecamatan Samarinda Ilir Tahun 2023

Abstract

Data mining is the process of extracting useful information and patterns from very large amounts of data. Based on the task or work performed, data mining is divided into cluster analysis, association analysis, anomaly detection, and predictive modeling. Predictive modeling consists of two types, namely regression and classification. Classification is a method for determining the membership of an object in a class based on available data. There are several methods for classification, one of which is naïve Bayes with the advantages of being easy to build and having good performance. This research aims to determine the results of the accuracy of the naïve Bayes classification on data on the welfare status of beneficiary households in Samarinda Ilir District in 2023. Based on the research results, it can be seen that the accuracy level of the naïve Bayes classification on this data is 0.8316 or 83.16%. The results of accuracy measurements show that the naïve Bayes classification of this data has a fairly high level of accuracy.