Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network (studi kasus : peramalan harga minyak mentah)

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Sri Herawati M Latif

Abstract

Abstrak Metode runtun waktu cocok digunakan ketika akan memeriksa setiap pola data secara sistematis dan memiliki banyak variabel bebas, seperti pada kasus harga minyak mentah. Salah satu penelitian yang memanfaatkan metode runtun waktu adalah integrasi antara Ensemble Empirical Mode Decomposition (EEMD) dan jaringan syaraf berdasarkan algoritma Polak-Ribie Conjugate Gradient (PCG). Namun, PCG memerlukan pengaturan parameter bebas dalam proses pembelajarannya. Sementara, parameter yang sesuai sangat dibutuhkan  untuk mendapatkan hasil peramalan yang akurat. Penelitian ini mengusulkan integrasi antara EEMD dan Generalized Regression Neural Network (GRNN). GRNN memiliki keunggulan, yaitu: tidak memerlukan pengaturan parameter dan proses pembelajaran yang cepat. Untuk evaluasi, kinerja metode EEMD-GRNN dibandingkan  dengan GRNN. Hasil eksperimen menunjukkan bahwa metode EEMD-GRNN menghasilkan peramalan yang lebih baik dari GRNN.


Kata kunci Peramalan Harga Minyak Mentah, EEMD, GRNN


 

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How to Cite
HERAWATI, Sri; LATIF, M. Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network. JURNAL INFOTEL, [S.l.], v. 8, n. 2, p. 132-137, nov. 2016. ISSN 2460-0997. Available at: <http://ejournal.st3telkom.ac.id/index.php/infotel/article/view/124>. Date accessed: 28 mar. 2017. doi: https://doi.org/10.20895/infotel.v8i2.124.
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