BibTex Citation Data :
@article{JSINBIS046, author = {Ilham Sayekti}, title = {Pengujian Model Jaringan Syaraf Tiruan Untuk Kualifikasi Calon Mahasiswa Baru Program Bidik Misi}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {2}, number = {3}, year = {2012}, keywords = {}, abstract = { Testing of neural network models for qualified new students Bidik Misi program is a software program that is built by using backpropagation neural network (ANN-BP) is used for the purpose of scholarship recipients qualify Bidik Misi of incoming freshmen at Semarang State Polytechnic . By using an 8 input variables such as parental occupation, parental income, parental education, number of dependents and academic values, with each variable consists of several different parameters, and 1 output variable result is rejected or accepted. Through a series of tests by combining the network parameters, in order to get the optimal results of neural networks, the best results are obtained logsig and purelin activation function. As research material used data from the 127 students who signed up as a potential recipient of a scholarship Bidik Misi. From some data, 50 data used as training data (learning), and 77 are used as test data, obtained results that a system built by the backpropagation neural network was able to qualify the scholarship recipients Bidik Misi success rate reached 99.21%. Keyword s : Artificial neural network; Backpropagation; Bidik Misi; Kualifikasi }, issn = {2502-2377}, pages = {146--150} doi = {10.21456/vol2iss3pp146-150}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/046} }
Refworks Citation Data :
Testing of neural network models for qualified new students Bidik Misi program is a software program that is built by using backpropagation neural network (ANN-BP) is used for the purpose of scholarship recipients qualify Bidik Misi of incoming freshmen at Semarang State Polytechnic . By using an 8 input variables such as parental occupation, parental income, parental education, number of dependents and academic values, with each variable consists of several different parameters, and 1 output variable result is rejected or accepted. Through a series of tests by combining the network parameters, in order to get the optimal results of neural networks, the best results are obtained logsig and purelin activation function. As research material used data from the 127 students who signed up as a potential recipient of a scholarship Bidik Misi. From some data, 50 data used as training data (learning), and 77 are used as test data, obtained results that a system built by the backpropagation neural network was able to qualify the scholarship recipients Bidik Misi success rate reached 99.21%.
Keywords : Artificial neural network; Backpropagation; Bidik Misi; Kualifikasi
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