Modifikasi Fruit Fly Optimiziation Algorithm untuk Optimasi General Regression Neural Network  pada Kasus Prediksi Time-Series

Ni Putu Novita Puspa Dewi; Ni Ketut Kertiasih; Ni Luh Dewi Sintiari

doi:10.23887/janapati.v11i3.54521

Authors

Ni Putu Novita Puspa Dewi Universitas Pendidikan Ganesha
Ni Ketut Kertiasih Prodi Teknologi Rekayasa Perangkat Lunak, Universitas Pendidikan Ganesha
Ni Luh Dewi Sintiari Prodi Ilmu Komputer, Universitas Pendidikan Ganesha

DOI:

https://doi.org/10.23887/janapati.v11i3.54521

Keywords:

Optimasi, GRNN, FOA, immune algorithm

Abstract

FOA merupakan algoritma optimasi swarm intelligence yang dikenal unggul dan cenderung sederhana untuk diimplementasikan, namun algoritma ini diketahui sulit untuk memecahkan masalah optimasi nonlinier berdimensi tinggi dan mudah jatuh ke dalam optimum lokal. Untuk mengatasi kekurangan ini, immune algorithm digunakan untuk menyeimbangkan kekurangan FOA guna meningkatkan efisiensi pencarian. Penelitian ini bertujuan untuk menggabungkan algoritma optimasi FOA dengan immune algorithm untuk digunakan mengoptimasikan model prediksi GRNN. Model hybrid dari GRNN dan FOA modifikasi (IAFOA) akan diuji-coba terhadap beberapa dataset time-series di berbagai domain. Performanya dibandingkan dengan model FOA basic untuk melihat dampak jelas dari proses modifikasi tersebut terhadap performa model GRNN dalam melakukan prediksi. Hasil pengujian menunjukkan bahwa eror prediksi RMSE dan MAE dari IAFOA A pada 4 kasus training dan testing dan 1 kasus lebih unggul pada proses training. Berdasarkan pengujian yang dilakukan kepada 3 dataset (6 kasus), IAFOA menghasilkan rata-rata eror prediksi lebih kecil yaitu RMSE sebesar 35348.63 dan MAE 26699.02 dibandingkan FOA dengan rata-rata eror prediksi secara berturut-turut 35792.59 dan 26967.12.

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