PREDIKSI PENGGUNAAN BANDWIDTH MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK

  • Jefri Radjabaycolle Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
  • Reza Pulungan Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
DOI: https://doi.org/10.30598/barekengvol10iss2pp127-135
Keywords: Prediksi, Bandwidth, jaringan syaraf tiruan (JST), elman recurrent neural network

Abstract

Jaringan Syaraf Tiruan (JST) sering dipakai dalam menyelesaikan permasalahan tertentu seperti prediksi, klasifikasi, dan pengolahan data. Berdasarkan hal tersebut, dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam prediksi penggunaan bandwidth. Sistem yang dikembangkan dapat digunakan untuk memprediksi pengunaan bandwidth dengan menerapkan Elman Recurrent Neural Network (ERNN). Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi.. Vektor input yang digunakan menggunakan windows size. Hasil penelitian dengan menggunakan target error sebesar 0.001 menunjukkan nilai MSE terkecil yaitu pada windows size 11 dengan nilai 0.002833. Kemudian dengan menggunakan 13 neuron pada hidden layer diperoleh nilai error paling optimal (minimum error) sebesar 0.003725.

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References

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Published
2016-12-01
How to Cite
[1]
J. Radjabaycolle and R. Pulungan, “PREDIKSI PENGGUNAAN BANDWIDTH MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK”, BAREKENG: J. Math. & App., vol. 10, no. 2, pp. 127-135, Dec. 2016.
Issue
Vol 10 No 2 (2016): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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