Application of Data Mining to Classify Receiving Social Assistance Using the Naïve Bayes Method

Abdul Wahid; Fauzan  Azim; Faiz  Firdausi

doi:10.31967/inside.v1i2.881

Abdul Wahid
Fauzan Azim Institut Teknologi dan Sains Mandala
Faiz Firdausi Institut Teknologi dan Sains Mandala

DOI: https://doi.org/10.31967/inside.v1i2.881

Keywords: Naïve Bayes Algorithm, Acceptance of Social Assistance, RapidMiner

Abstract

The application of Naïve Bayes in classifying potential recipients of Non-Cash Food Assistance (BPNT) in Grujugan Kidul Village was made to solve problems in the process of distributing non-cash food assistance programs that had not run optimally and had not been on target. The purpose of this study is to find out how the Naïve Bayes Algorithm is applied in classifying Non-Cash Food Assistance Recipients in Grujugan Kidul Village so as to obtain optimal results. The method used in this study is the Naïve Bayes classification method. The Naïve Bayes algorithm is proven to have good performance in predictions, and produces high accuracy values. The stages of data analysis were carried out based on the CRISP-DM method while the algorithm testing was carried out on the RapidMiner 9.10.001 software as a comparison between manual calculations and software calculations. The results of the accuracy in this study were 89.19% from 8:2 Comparison of 80% test data, 20% test data.