JUITA : Jurnal Informatika
JUITA Vol. 10 No. 1, May 2022

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

Murinto Murinto (Universitas Ahmad Dahlan Yogayakarta)
Miftahurrahma Rosyda (Universitas Ahmad Dahlan Yogayakarta)

Article Info

Publish Date
31 May 2022


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 parameters 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.

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Journal Info





Computer Science & IT


UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah ...