ANALISIS METODE RBF-NN DAN GRNN PADA PERAMALANMATA UANG EUR/USD
Keywords:
EUR/USD; RBF-NN; Algoritma Genetika; GRNN; MAPE; spread; daily high; daily lowAbstract
Penelitian ini merupakan lanjutan dari penelitian sebelumnya tentang peramalan EUR/USDmenggunakan metode RBF-NN (Radial Basis Function – Neural Network) yang dioptimasi denganAlgoritma Genetika. Metode yang ditambahkan adalah GRNN (Generalized Regression NeuralNetwork). Sistem RBF-NN dapat diterapkan pada data dengan karakteristik nonlinear dan fluktuatifseperti data EUR/USD, sementara GRNN dapat bekerja dengan baik jika data training tersedia dalamjumlah banyak. Tingkat keakuratan dari peramalan ditunjukkan lewat nilai MAPE (Mean AbsolutPercentage Error).Dari hasil percobaan, metode GRNN tidak memiliki nilai MAPE yang lebih baik daripada RBF-NN baikpada data daily low maupun data daily high. Teknik pencarian algorima genetika di dekat bobot RBFNN terbukti lebih efektif daripada pendekatan fungsi GRNN dengan spread kecil pada kasus matauang EUR/USD.
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