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All Journal JUITA : Jurnal Informatika Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer)
Miftahurrahma Rosyda
Universitas Ahmad Dahlan, Yogyakarta
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Journal : JUITA : Jurnal Informatika

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Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network Murinto Murinto; Miftahurrahma Rosyda
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1285.081 KB) | DOI: 10.30595/juita.v10i1.12573

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 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.
Co-Authors Avanti Vera Risti Pramudyani Ganjar Tata Pangestu Murinto Murinto Prima Suci Rohmadheny Raihan Aqila Taufik Tri Suryo Wahyu Aji