R.E. Putra, A.I. Nurhidayat, A.Y. Wicaksono
One of new student admission pathways at Universitas Negeri Surabaya (Unesa) is through the Indonesian National Public University Admission. This path is quite favourable for academic or vocational high school students who want to study at Unesa so that the number of participants for this selection program can reach up to ten thousand people every year. The large number of applicants makes the selection process more complex. Meanwhile, Unesa still uses the concept of weighting criteria in determining the result. One of the constraints in weighting process is the absence of optimal pattern or weight. This paper discusses a supervised learning approach to make determining process pattern which replaces manual weighting criteria. The supervised learning method used in this research was the Neural Network with multilayer perceptron. This research showed sufficient result which can be seen from the high accuracy rate (89.56%). The accuracy rate is enough to decide which participants who pass or fail the national admission. This system can be used as a prediction for the following years. © Published under licence by IOP Publishing Ltd.
Department of Informatics Engineering, Universitas Negeri Surabaya, Ketintang, Surabaya, Indonesia
