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Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 10 No. 2 (2016) · pp. 161–172

DOI
10.22146/ijccs.16628
ISSN
2460-7258, 1978-1520
Published
2016-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features. For the process of recognition based on the features that have been extracted, used neural network methods with backpropagation learning algorithm. From the neural-network training, the best accuracy in classifying varieties of chili with minimum error 0.001 by providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden layer foreach of various feature. To conduct cross-validation testing with k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09% with k=4.

Keywords

Collaboration network of the authors

40 authors · 45 co-authorship links · 13 across different organisations · showing the 40 most connected

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

  • Universitas Gadjah Mada
  • State University of Semarang
  • Universitas Pendidikan Ganesha
  • State University of Medan
  • Duta Wacana Christian University
  • Sumatera Institute of Technology
  • Politeknik Negeri Kupang
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kridalaksana Harsa AwangAffiliation unknown1
Zaenal AbidinState University of Semarang1
Yustina RetnoAffiliation unknown1
Abriyono AbriyonoAffiliation unknown1
I Gede Aris GunadiUniversitas Pendidikan Ganesha1
Rocky Yefrenes DillakUniversitas Gadjah Mada1
Ikhwan RusliantoUniversitas Gadjah Mada1
Rahmi HidayatiUniversitas Gadjah Mada1
Salman AliajiUniversitas Gadjah Mada1
Refta ListiaUniversitas Gadjah Mada1
Mahmuddin YunusAffiliation unknown1
Hermawan SyahputraState University of Medan1
Sri SuwarnoDuta Wacana Christian University1
Rajif Agung YunmarSumatera Institute of Technology1
Nurmiyati TamatjitaUniversitas Gadjah Mada1
La Ode Hasnuddin S. SagalaUniversitas Gadjah Mada1
Nicodemus Mardanus SetiohardjoPoliteknik Negeri Kupang1
I Putu Adi PratamaUniversitas Gadjah Mada1
Herman HermanUniversitas Gadjah Mada1
Dian Hafidh ZulfikarUniversitas Gadjah Mada1
Made Leo RadhityaInstitut Bisnis dan Teknologi Indonesia1
Pawit RiantoAffiliation unknown1
Adri PriadanaUniversitas Gadjah Mada1
Ade NurhopipahUniversitas Amikom Purwokerto1
Aina MusdholifahUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Angga Maulana PurbaUniversitas Gadjah Mada1
Mohammad Edi WibowoAffiliation unknown1
Wahyono WahyonoUniversitas Gadjah Mada1
Abdul KholikUniversitas Gadjah Mada1
Anny Kartika SariUniversitas Gadjah Mada1
Alfian Abdul JalidUniversitas Gadjah Mada1
Nicolaus Euclides Wahyu NugrohoUniversitas Gadjah Mada1
Bryan Andi GerrardoUniversitas Gadjah Mada1
Nai Wei LoNational Taiwan University of Science and Technology1
Panji BintoroUniversitas Gadjah Mada1
Gregorius Adi PradanaUniversitas Gadjah Mada1
Kusrini KusriniUniversitas Amikom Yogyakarta1

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://journal.ugm.ac.id/ijccs/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">Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network</dc:title>
	<dc:creator>Syaban, Kharis</dc:creator>
	<dc:creator>Harjoko, Agus</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">leaf; chili; morphological; moment-invariant; backpropagation</dc:subject>
	<dc:description xml:lang="en-US">Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features. For the process of recognition based on the features that have been extracted, used neural network methods with backpropagation learning algorithm. From the neural-network training, the best accuracy in classifying varieties of chili with minimum error 0.001 by providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden layer foreach of various feature. To conduct cross-validation testing with k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09% with k=4.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2016-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/16628</dc:identifier>
	<dc:identifier>10.22146/ijccs.16628</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 10, No 2 (2016): July; 161-172</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/16628/11686</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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