SISTEM CERDAS EVALUASI KELAYAKAN MAHASISWA MAGANG MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK (ERNN) ( Studi Kasus : Jurusan Manajemen Informatika, Fakultas Teknik dan Kejuruan, Universitas Pendidikan Ganesha, Singaraja-Bali ) - , Agus Aan Jiwa Permana ([Yogyakarta] : Universitas Gadjah Mada, 2013)
Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing.