Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Aiti: Jurnal Teknologi Informasi
Tad Gonsalves
Dept. Of Information and Communication Sciences, Sophia University, Japan
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Neuroevolution untuk optimalisasi parameter jaringan saraf tiruan Hindriyanto Dwi Purnomo; Tad Gonsalves; Teguh Wahyono; Pratyaksa Ocsa Nugraha Saian
AITI Vol 20 No 2 (2023)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v20i2.125-134

Abstract

Artificial Neural Network is a supervised learning method for various classification problems. Artificial Neural Network uses training data to identify patterns in the data; therefore, training phase is crucial. During this stage, the network weight is adjusted so that they can recognize patterns in the data. In this research, a neuroevolution approach is proposed to optimize artificial neural network parameters (weight) Neuroevolution is a combination of evolutionary algorithms, including various metaheuristics algorithms, to optimize neural network parameters and configuration. In particular, this research implemented particle swarm optimization as the artificial neural network optimizer. The performance of the proposed model was compared to backpropagation, which uses gradient information to adjust the neural network parameter. There are five datasets used as the benchmark problems. The datasets are iris, wine, breast cancer, ecoli, and wheat seeds. The experiment results show that the proposed method has better accuracy than the backpropagation in three out of five problems and has the same accuracy in two problems. The proposed method is also faster than the backpropagation method in all problems. These results reveal that neuroevolution is a promising approach to improving the performance of artificial neural networks. Further studies are needed to explore more benefits of this approach.
Co-Authors Hindriyanto Dwi Purnomo Pratyaksa Ocsa Nugraha Saian Teguh Wahyono