Implementation Of Data Mining With Naïve Bayes For Prediction Of Direct Cash Assistance Recipients (Blt) Xyz Village Citizens

Abstract

Xyz Village officials carry out a program from the government to distribute Direct Cash Assistance (BLT) to underprivileged people in Medan Tembung District. The BLT program is a government assistance program in the form of giving cash to the poor. Implementation of the program aims to improve the welfare of the poor. Currently, it is considered that the BLT distribution is still not on target and does not meet the criteria which are the benchmarks for the people who will receive the assistance. The Naïve Bayes method was used in this case study to classify beneficiary communities in order to more easily select people who really deserved to receive BLT. This study uses 20 data and 5 criteria including: name, age, gender, status, income, and beneficiary status. After conducting research with a total of 20 data and using the Naïve Bayes method, the results obtained were that 45% of the residents of Xyz Village were eligible to receive BLT and 55% of the residents were not eligible to receive BLT. Keywords: Data Mining, Naïve Bayes Algorithm, Direct Cash Assistance