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All Journal Jurnal Mantik
Sariyasa Sariyasa
Universitas Pendidikan Ganesha, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Batch size and learning rate effect in covid-19 classification using CNN Ni Komang Rai Mirayanti; Sariyasa Sariyasa; I Gede Aris Gunadi
Jurnal Mantik Vol. 7 No. 3 (2023): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i3.4177

Abstract

This study evaluated the performance of a Convolutional Neural Network (CNN) model in classifying CT-Chest images of COVID-19 and non-COVID patients. The primary focus was to determine the influence of learning rate and batch size on the model's effectiveness. This research used 698 datas from Covid-19 and NonCovid-19 CT-Chest Image. Those dataset was obtained from medRxiv dan bioRxiv and has been approved by radiology expert in Tongji Hospital, China. In this research, COVID-16 dataset was classified by CNN with different batch sizes and learning rates for each iteration. Batch size used in this study were 1, 2, 4, 8, 16, 32, 64, and 128 with learning rates 0,00001; 0,0001; 0,001; 0,01; 0,1 and 1. This study found that this study showed that batch size and learning rate have a positive effect on CNN performance. The lower the learning rate, the lower the batch size will allow the network in the CNN model to perform better in classifying COVID-19 CT-Chest. In Addition, the best batch size for the classification is 64 with learning rate 0,01. These findings provided important insights into how parameters such as learning rate and batch size impacted the performance of the CNN model in classifying COVID-19 CT-Chest
Co-Authors I Gede Aris Gunadi Ni Komang Rai Mirayanti