Meminimalisasi Nilai Error Peramalan dengan Algoritma Extreme Learning Machine

Rachmad Hidayat; Suprapto Suprapto

doi:10.25077/josi.v11.n1.p187-192.2012

Rachmad Hidayat

Teknik Industri, Fakultas Teknik, Universitas Trunojoyo Madura

Suprapto Suprapto

Teknik Elektro, Fakultas Teknik, Universitas Brawijaya Malang

Keywords

Abstract

This study uses a machine learning algorithm which is the extreme model one of the new learning method of neural networks. Determining the value of forecasting based on actual value. Output generated more quickly in this process, because the learning done in a fast speed and better accuracy rate than conventional forecasting methods. Epoch using the parameter changes and changes in the range of accuracy of test results demonstrate the value of error is quite good during the system testing.
Keywords: neural networks, epoch, value of error.

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References

[1] J. Ai, “Optimasi Peramalan Pemulusan Exponensial satu Parameter Dengan Menggunakan Algoritma Non-Linear Programing”, Jurnal Teknologi Industri, Bandung. 1999. [2] G.B. Huang, , Q.Y. Zhu, and C.K. Siew, “Extreme Learning Machine: Theory and applications’, Neurocomputing, vol. 70, pp. 489-501, 2006. [3] G. Zhang, , B. E. Pattuwo, , and M.Y. Hu, “Forecasting with Artificial Neural Networks: The State of the Art”, International Journal of Forecasting, vol.14, pp. 35-62, 1998. [4] Z.L. Sun, T.M. Choi, , K.F. Au, and Y. Yu, “Sales Forecasting using Extreme Learning Machine with Application in Fashion Retailing”, Decision Support Systems, vol. 46, pp. 411-419, 2008. [5] N-Y. Liang. Classification of mental tasks from eeg signals Using extreme learning machine. School of electrical and electronic engineering, Nanyang technological University, Nanyang avenue, Singapore. 2006.

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Published

May 2, 2016

https://doi.org/10.25077/josi.v11.n1.p187-192.2012

How to Cite

[1]

R. Hidayat and S. Suprapto, “Meminimalisasi Nilai Error Peramalan dengan Algoritma Extreme Learning Machine”, J. Optimasi Sist. Ind., vol. 11, no. 1, pp. 187–192, May 2016.

Issue

Vol. 11 No. 1 (2012): Published in 1st April 2012

Section

Articles

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