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Optimization of 2D-CNN Setting for the classification of covid disease using Lung CT Scan

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 15 No. 2 (2022) · pp. 143–149

DOI
10.21609/jiki.v15i2.1083
ISSN
2502-9274, 2088-7051
Published
2022-07-02
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

RT-PCR is considered the best diagnostic tool. Previous studies have demonstrated the reliability of CNN in classifying classifications, but CNN requires a lot of training data. Meanwhile, at the CT Scan clinic, patients are limited. Therefore, exploration of 2D-CNN settings is proposed to optimize CNN performance on limited data. We compare: (1) activation models, (2) output shapes per layer, (3) dropout layers, and (4) early stopping values. The test results show that RELU activation is better than Sigmoid. Rescaling (128x128) is better for scala (64x64) and (256x256) which affects the output shape model of each layer. In this learning stage, the use of dropouts in the CNN architecture achieves robust accuracy than the architecture that ignores dropouts. The use of 15 early stoppings is better than other values compared. 20 images of pneumonia and 20 images of covid have been tested using the proposed method and achieved 87.50% accuracy, 80.00% precision, 100% recall, and 99.89% F1-Score. Our method is superior to the the comparison method in terms of accuracy, precision, recall, and f1-score, which achieves 85%, 70%, 100%, and 82.35%, respectively.

Keywords

Collaboration network of the authors

13 authors · 26 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Amikom Yogyakarta
  • Institut Teknologi Sepuluh Nopember
  • State University of Malang
  • Universitas AKPRIND Indonesia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ema UtamiUniversitas Amikom Yogyakarta3
Andi SunyotoUniversitas Amikom Yogyakarta1
Mursyid ArdiansyahUniversitas Amikom Yogyakarta1
Suwanto RaharjoUniversitas AKPRIND Indonesia1
Ahmad Fikri IskandarUniversitas Amikom Yogyakarta1
Agung Budi PrasetyoUniversitas Amikom Yogyakarta1
Anggit Dwi HartantoUniversitas Amikom Yogyakarta1
Febri LiantoniInstitut Teknologi Sepuluh Nopember1
Tri Hadiah MuliawatiInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Optimization of 2D-CNN Setting for the classification of covid disease using Lung CT Scan</dc:title>
	<dc:creator>Kirana, Kartika Candra</dc:creator>
	<dc:creator>Wibawanto, Slamet</dc:creator>
	<dc:creator>Hamdan, Ahmad</dc:creator>
	<dc:creator>hidayat, wahyu nur</dc:creator>
	<dc:subject xml:lang="en-US">2D-CNN</dc:subject>
	<dc:subject xml:lang="en-US">Covid</dc:subject>
	<dc:subject xml:lang="en-US">Lung CT Scan</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:description xml:lang="en-US">RT-PCR is considered the best diagnostic tool. Previous studies have demonstrated the reliability of CNN in classifying classifications, but CNN requires a lot of training data. Meanwhile, at the CT Scan clinic, patients are limited. Therefore, exploration of 2D-CNN settings is proposed to optimize CNN performance on limited data. We compare: (1) activation models, (2) output shapes per layer, (3) dropout layers, and (4) early stopping values. The test results show that RELU activation is better than Sigmoid. Rescaling (128x128) is better for scala (64x64) and (256x256) which affects the output shape model of each layer. In this learning stage, the use of dropouts in the CNN architecture achieves robust accuracy than the architecture that ignores dropouts. The use of 15 early stoppings is better than other values compared. 20 images of pneumonia and 20 images of covid have been tested using the proposed method and achieved 87.50% accuracy, 80.00% precision, 100% recall, and 99.89% F1-Score. Our method is superior to the the comparison method in terms of accuracy, precision, recall, and f1-score, which achieves 85%, 70%, 100%, and 82.35%, respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2022-07-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>text/plain</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1083</dc:identifier>
	<dc:identifier>10.21609/jiki.v15i2.1083</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 15 No. 2 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 143-149</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v15i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1083/466</dc:relation>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1083/484</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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