Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Machine Learning dengan Teknik Deep Learning

Jurnal Informatika: Jurnal Pengembangan IT

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Field Value
 
Title Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Machine Learning dengan Teknik Deep Learning
 
Creator Martanto, Martanto; STMIK IKMI Cirebon
Ali, Irfan; STMIK IKMI Cirebon
Mulyawan, Mulyawan; STMIK IKMI Cirebon
 
Subject
Prediction; Graduation; Machine Learning; Deep Learning
 
Description 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%.
 
Publisher Politeknik Harapan Bersama
 
Contributor
 
Date 2019-12-19
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ejournal.poltektegal.ac.id/index.php/informatika/article/view/1877
10.30591/jpit.v4i2-2.1877
 
Source Jurnal Informatika: Jurnal Pengembangan IT; Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknologi Pembelajaran; 191-194
2548-9356
2477-5126
10.30591/jpit.v4i2-2
 
Language eng
 
Relation http://ejournal.poltektegal.ac.id/index.php/informatika/article/view/1877/pdf_51
10.30591/jpit.v4i2-2.1877.g1119
 
Rights Copyright (c) 2020 Jurnal Informatika: Jurnal Pengembangan IT
http://creativecommons.org/licenses/by/4.0
 

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