Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako - Amat Damuri; Umbar Riyanto; Hengki Rusdianto; Mohammad Aminudin (JURIKOM (Jurnal Riset Komputer), 2021)
Poverty is one of the fundamental problems that is the center of attention of the government in a country. One of the important aspects to support the Poverty Reduction Strategy is the availability of accurate and targeted poverty data. 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 the recipients of basic food assistance. There are two classes of predictions for the recipients of the basic food assistance, namely eligible and not eligible. The data used for prediction is sample data from XYZ village. In this research, the nave Bayes algorithm is implemented and analyzed using a web-based application. From the results of the evaluation using the confusion matrix, the resulting accuracy for 135 training data with 40 testing data and seven attributes used resulted in an accuracy of 86%, recall of 85%, and precision of 88%.