PREDIKSI KEGAGALAN SISWA DALAM DATA MINING DENGAN MENGGUNAKAN METODE NAÏVE BAYES

Rumini Rumini (Universitas AMIKOM Yogyakarta)
Norhikmah Norhikmah (Universitas AMIKOM Yogyakarta)

Jurnal Mantik Penusa
Vol. 3 No. 1.1 (19): Manajemen dan Ilmu Komputer

In an effort to improve the quality of a nation, there is no other way except through improving the quality of education. The success and failure of students' classes in the study determined many factors that influence it. The results of this study are using the naïve bayes data mining method which is used to predict student failures in their studies as well as influential factors including failure, traveltime, internet, romantic, freetime, go-out, health, and absence. Naïve Bayes algorithm is an algorithm that can be used to predict using probability theory with a high degree of accuracy. Naïve Bayes algorithm testing uses WEKA tool which produces an accuracy of 77.22 from 395 datasets. This algorithm is used to predict student class failures