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All Journal Jurnal ICT : Information Communication & Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Irfan Ali, Irfan
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Computer Science & IT

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Journal : Jurnal ICT : Information Communication

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Optimasi Parameter Artificial Neural Network Menggunakan Algoritma Genetika Untuk Prediksi Kelulusan Mahasiswa Ali, Irfan; Sularto, Lana
Jurnal ICT : Information Communication & Technology Vol 18, No 1 (2019): JICT-IKMI, Juli 2019
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v18i1.52

Abstract

It is difficult to predict student graduation status in a college. Higher education needs to predict student behavior from active students so that it can be seen the failure factor of students who do not graduate on time. Data mining classification techniques used to predict students are using artificial neural networks. Artificial neural network is one method to predict student graduation. This researcher tries to apply artificial neural network methods using genetic algorithms to predict student graduation. In this study using the learning rate parameter 0.1 with optimization using genetic algorithms then evaluating to get accuracy. The results of this study get an accuracy value for artificial neural network models of 71.48% and accuracy for artificial neural network models based on genetic algorithms by 99.33% with an accuracy difference of 27.85%.
Co-Authors Dikananda, Arif Rinaldi Lana Sularto Lestari, Gifthera Dwi Odi Nurdiawan R, Nining Rohmat, Cep Lukman