Optimasi Nilai Bobot Algoritma Backpropagation Neural Network Dengan Algoritma Genetika

Moh. Dasuki

Abstract


Backprogation Neural Network (BPNN) merupakan salah satu metode peramalan yang sudah banyak dilakukan kemampuan Artificial Neural Network dalam melakukan suatu pembelajaran terbukti mempunyai kinerja yang cukup baik, namun Backpropagation memiliki dua kelemahan utama yaitu kecepatan convergence yang buruk dan tidak stabil, hal ini disebabkan karena resiko terjebaknya pada lokal minimum. Dua kelemahan itu dipengaruhi bobot awal yang dipilih secara random. Algoritma Genetika akan digunakan untuk menentukan bobot serta bias awal terhadap parameter Backpropagation sehingga bisa mendapatkan kemampuan belajar yang baik. Hasil yang diperoleh dari hasil experiment yang sudah dilakukan menggunakan Jaringan Syaraf Tiruan yang di optimasi menggunakan Algoritma Genetika menghasilkan nilai Root Mean Sequare Error yang lebih baik dibandingkan dengan Jaringan Syaraf Tiruan tampa optimasi.

Keywords


Backprogation Neural Network, Genetic Algorithm

References


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DOI: https://doi.org/10.32528/justindo.v6i1.5280

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