Pengembangan Model Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Mahasiswa Baru di PTS Surabaya (Studi Kasus Universitas Wijaya Putra)

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Alven Safik Ritonga Suryo Atmojo

Abstract

Artificial Neural Network and data time series can use for good forecasting method. Artificial Neural Network is a method whose working principle is adapted from mathematical models in humans or biological neural.Neural networks are characterized by; (1)pattern of connections between the neurons(called architecture), (2)determine the weight of the connection (called training or learning), and (3)activation function.The objective of this research is to get the best artificial neural network architecture, compare two method of Backpropagation Artificial Neural Network with Radial Basis Function Artificial Neural Network (RBF).This research is a research using actual data (true experimental). This research was conducted at Wijaya Putra University Surabaya, using secondary data obtained from 2012 to 2016.The result of the research shows that there is a difference between RBF ANN method and the method of Backpropagation ANN, obtained statistical index of RBF ANN, MAE = 0.0074, RMSE = 0.0096, error = 12.6532%. Statistical index of Backpropagation ANN, MAE = 0.2129, RMSE = 0, 2752, error = 13.3217%.

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How to Cite
RITONGA, Alven Safik; ATMOJO, Suryo. Pengembangan Model Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Mahasiswa Baru di PTS Surabaya (Studi Kasus Universitas Wijaya Putra). Jurnal Ilmiah Teknologi Informasi Asia, [S.l.], v. 12, n. 1, p. 15-24, jan. 2018. ISSN 2580-8397. Available at: <https://jurnal.stmikasia.ac.id/index.php/jitika/article/view/213>. Date accessed: 22 apr. 2021. doi: https://doi.org/10.32815/jitika.v12i1.213.
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