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DETECTION OF DISEASE ON CORN PLANTS USING CONVOLUTIONAL NEURAL NETWORK METHODS

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 12 No. 1 (2019) · pp. 51–56

DOI
10.21609/jiki.v12i1.695
ISSN
2502-9274, 2088-7051
Published
2019-03-01
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Deep Learning is still an interesting issue and is still widely studied. In this study Deep Learning was used for the diagnosis of corn plant disease using the Convolutional Neural Network (CNN) method, with a total dataset of 3.854 images of diseases in corn plants, which consisted of three types of corn diseases namely Common Rust, Gray Leaf Spot, and Northern Leaf Blight. With an accuracy of 99%, in detecting disease in corn plants.

Keywords

Collaboration network of the authors

3 authors · 3 co-authorship links

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  • Nasional University
  • Affiliation unknown
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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">DETECTION OF DISEASE ON CORN PLANTS USING CONVOLUTIONAL NEURAL NETWORK METHODS</dc:title>
	<dc:creator>Hidayat, Ardi</dc:creator>
	<dc:creator>Darusalam, Ucuk</dc:creator>
	<dc:creator>Irmawati, Irmawati</dc:creator>
	<dc:subject xml:lang="en-US">Convolutional Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Corn Plants</dc:subject>
	<dc:subject xml:lang="en-US">Deep Learning.</dc:subject>
	<dc:description xml:lang="en-US">Deep Learning is still an interesting issue and is still widely studied. In this study Deep Learning was used for the diagnosis of corn plant disease using the Convolutional Neural Network (CNN) method, with a total dataset of 3.854 images of diseases in corn plants, which consisted of three types of corn diseases namely Common Rust, Gray Leaf Spot, and Northern Leaf Blight. With an accuracy of 99%, in detecting disease in corn plants.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2019-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/695</dc:identifier>
	<dc:identifier>10.21609/jiki.v12i1.695</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 12 No. 1 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 51-56</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v12i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/695/411</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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