Penerapan Data Mining Untuk Prediksi Penerima Bantuan Pangan Non Tunai (Bpnt) Di Desa Wanacala Menggunakan Metode Naïve Bayes

Abstract

The Non-Cash Food Assistance Program held by the government is often not on target due to many factors, one of which is the number of criteria that must be considered to be a decision of beneficiaries. Of the eleven criteria set requires the right algorithm to perform calculations so that the results given are more accurate. Naïve Bayes algorithm is a method for classification using probability theory that has a high degree of accuracy. Naïve Bayes algorithm testing uses Rapid Miner tools that produce an accuracy rate of 96% of the 50 data provided. This algorithm is right for the selection of recipients of non-cash food assistance. There are 2 classes that are needed, namely Worthy and Not Eligible.

Keywords: Classification, Naïve Bayes, Rapid Miner, Non-Cash Food Aid