Penerapan Data Mining Untuk Prediksi Penerima Bantuan Pangan Non Tunai (BPNT) 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 many criteria that must be considered in making a decision to receive assistance. Of the eleven criteria set, it requires the right algorithm to perform the calculation so that the results given are more accurate. The Naïve Bayes algorithm is a method for classification using probability theory which has a high degree of accuracy. Testing the Naïve Bayes algorithm uses the Rapid Miner tool which produces an accuracy rate of 96% from the 50 data provided. This algorithm is appropriate for selecting recipients of non-cash food assistance. There are 2 classes needed, namely Eligible and Ineligible.

Keywords: Naïve Bayes, Rapid Miner