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Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 2 (2019) · pp. 189–198

DOI
10.22146/ijccs.42036
ISSN
2460-7258, 1978-1520
Published
2019-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.

Keywords

Collaboration network of the authors

16 authors · 24 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 Gadjah Mada
  • University of Brawijaya
  • National Research and Innovation Agency
  • Sumatera Institute of Technology
  • Universitas Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arief IchwaniSumatera Institute of Technology1
Windyaning UstyannieUniversitas Gadjah Mada1
Devi Valentino WaasUniversitas Gadjah Mada1
Wahyono WahyonoUniversitas Gadjah Mada1
Yaqutina Marjani SantosaUniversitas Gadjah Mada1
Agung PambudiUniversitas Gadjah Mada1
Nasution NasutionUniversitas Gadjah Mada1
Yusi Tyroni MursityoUniversity of Brawijaya1
Muhammad GhiffariUniversity of Brawijaya1
Yurizka Melia SariUniversitas Negeri Surabaya1
Janu ArlinwibowoNational Research and Innovation Agency1
Gupita Nadindra FatimaAffiliation unknown1
Dwi PurwokoNational Research and Innovation Agency1

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">Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image</dc:title>
	<dc:creator>Dzulqarnain, Muhammad Faqih</dc:creator>
	<dc:creator>Suprapto, Suprapto</dc:creator>
	<dc:creator>Makhrus, Faizal</dc:creator>
	<dc:subject xml:lang="en-US">Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">sorting salak fruit; Convolutional Neural Network; digital image; increased accuracy; parameter</dc:subject>
	<dc:description xml:lang="en-US">Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.</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>2019-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/42036</dc:identifier>
	<dc:identifier>10.22146/ijccs.42036</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 2 (2019): April; 189-198</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/42036/24374</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 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>
</oai_dc:dc>