Implementasi Data Mining Untuk Memprediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes Classifier Studi Kasus: Poltekkes Kemenkes RI Medan

Abstract

In this study, the prediction of student graduation at the Polytechnic of the Republic of Indonesia Ministry of Health can be seen from the accuracy in completing his studies. This can be seen at the final level / level 3 or semester 6. Prediction of graduation of these students can be completed by looking at the student data sample, the attributes that are determined and the final results based on the cumulative achievement index (GPA). To help the study program section in the search for data students graduate on time or not on time. This study uses the Naive Bayes Classifier method which is a simple probability classification by adding up the frequency and combination of values from the student dataset to be given, as well as the algorithm using the Bayes theorem and assuming adjusted attributes. Therefore an application was designed using WEKA (Waikato Environment for Knowledge Analysis).