Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

Murinto, Murinto and Rosyda, Miftahurrahma (2022) Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network. [Artikel Dosen]

Text HASIL CEK_Murinto_Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network.pdf Download (1MB)

Abstract

The convolutional neural network (CNN) is a technique that is often used in deep learning. Various models have been proposed and improved for learning on CNN. When learning with CNN, it is important to determine the optimal parameters. This paper proposes an optimization of CNN arameters using logarithm decreasing inertia weight (LogDIW). This paper is used two datasets, i.e., MNIST and CIFAR-10 dataset. The MNIST learning experiment, the CIFAR-10 dataset, compared its accuracy with the CNN standard based on the LeNet-5 architectural model. When using the MNIST dataset, CNN's baseline was 94.02% at the 5th epoch, compared to CNN's LogDIWPSO, which improves accuracy. When using the CIFAR-10 dataset, the CNN baseline was 28.07% at the 10th epoch, compared to the
LogDIWPSO CNN accuracy of 69.3%, which increased the accuracy.

Item Type:	Artikel Dosen
Keyword:	CIFAR-10, CNN, logarithm decreasing inertia weight, MNIST
Subjects:	Q Science > Q Science (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	murinto murinto
Date Deposited:	18 Apr 2023 09:48
Last Modified:	23 Sep 2023 05:57
URI:	http://eprints.uad.ac.id/id/eprint/43007

Actions (login required)

View Item