IMPLEMENTATION OF DATA MINING WITH NAIVE BAYES ALGORITHM FOR FEASIBILITY CLASSIFICATION OF WEB-BASED FOOD ASSISTANCE RECIPIENTS

Abstract

Non-Cash Food Assistance, which stands for BPNT or the Sembako Card, is a social assistance program that is distributed to the poor to meet basic food needs. Through the Sembako Card social assistance program, poor people who are recipients of basic necessities can receive regular assistance of Rp. 200,000 per month or Rp. 2.4 million for a year. In its implementation in various regions, it is still not optimal because many of the Non-Cash Food Assistance (BPNT) have not been right on target in determining who the recipients of Non-Cash Food Assistance (BPNT) are entitled or eligible to get it. The concept of Data Mining will make it easier to overcome these problems. By using the Naive Bayes Classification Algorithm, this research is expected to help the government, especially the Social Service, in determining who the recipients of Non-Cash Food Assistance (BPNT) are entitled to receive. Naïve Bayes is one method that can be used to classify data. The results of the classification carried out will later help aid managers to make decisions regarding the classification of determining recipients of Non-Cash Food Assistance (BPNT). There are two classes of non-cash food assistance recipients (BPNT) predictions, namely feasible and not feasible.