IMPLEMENTASI DATA MINING MENGGUNAKAN METODE NAIVE BAYES DENGAN FEATURE SELECTION UNTUK PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU

Abstract

The Education Efficiency Rate (AEE) is one of the parameters of the quality of the education program. The quality is measured based on 7 main standards, one of which is students and graduates. Meanwhile, to predict students' graduation rates accurately based on manually owned data set characteristics is very difficult. Data Mining by Naïve Bayes method was chosen to find patterns in analyzing and predicting timely graduation of students. As for the test will be done by comparing the initial dataset and dataset characteristics using the algorithm attribute selector Gain Ratio Attribute with the help of tools WEKA. The results showed that there was a difference to the accuracy of the results, and the larger ROC or AUC curves on the dataset characteristics using the selector attribute by using the Gain Ratio Attribute, although not very significant. And the result of this research yield 81% accuracy level with precision equal to 83.563% and recall 88.41%. The method used is included in Good Classification and will become the reference of the college management side, to address the problems that may arise in the decrease of the quality of education (e.g. decrease ratio of lecturers with students).