Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Machine Learning dengan Teknik Deep Learning - Martanto Martanto; Irfan Ali; Mulyawan Mulyawan (Politeknik Harapan Bersama, 2019)
The graduation rate of students on time at the Informatics Engineering study program STMIK IKMI Cirebon greatly affects the accreditation assessment. Graduation prediction is difficult to do, but many have done predictions using a variety of methods. Graduation prediction is needed in order to determine preventive policies for students who graduate not on time. The method used in this research is Machine learning with deep learning techniques. The data set used as many as 405 data of students who graduated on time or who were not on time. The research attributes used are the Nim attribute, the GPA value of students who have graduated and the status of graduating or not graduating. The results of this study are the level of accuracy using Machine Learning by 72.84%.