Implementasi Algoritma Rough Set Dan Naive Bayes Untuk Mendapatkan Rule Dalam Menyeleksi Pemohon Bantuan Fasilitas Rumah Ibadah (Studi Kasus : Pemerintah Kabupaten Pringsewu)

Abstract

In solving the problem of the accuracy of the selected algorithm if it is applied to a prototype application in predicting applicants for assistance with houses of worship facilities in Pringsewu District using data mining classification methods. In solving the problem using the rough set algorithm method and Naive Bayes from the results of the discussion carried out, it can draw conclusions Rough set algorithm and the resulting rule has the highest level of accuracy that is 92% Rough set algorithm model is included in the category of excellent classification and can be implemented in determining predictions more potential grant funding. The rules generated by the Rough set algorithm are applied in the prototype prediction of the grant of houses of worship grants with 92% accuracy of prototype verification testing results. Based on the accuracy of the resulting prototype shows that the methods and prototypes that are applied are good at predicting better results. Naïve Bayes algorithm has an accuracy level of 77% The Naïve Bayes algorithm model is included in the category of good classification and can be implemented in determining the prediction of grants but because the value of the rough set algorithm is higher then the naïve Bayes algorithm is not used to determine the prototype.