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Identification of Rice Variety Using Geometric Features and Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 3 (2019) · pp. 301–312

DOI
10.22146/ijccs.48203
ISSN
2460-7258, 1978-1520
Published
2019-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.

Keywords

Collaboration network of the authors

28 authors · 46 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
  • Politeknik Negeri Ketapang
  • National Central University
  • Muria Kudus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Retantyo WardoyoUniversitas Gadjah Mada4
Anny Kartika SariUniversitas Gadjah Mada3
Sri T. HartatiUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Fajar Ibnu TufeilAffiliation unknown1
Mujiono MujionoAffiliation unknown1
Annisa MaulianiUniversitas Gadjah Mada1
Ria AstriratmaUniversitas Gadjah Mada1
Agus HarjokoUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Maulida Ayu FitrianiUniversitas Gadjah Mada1
Damar RiyadiUniversitas Gadjah Mada1
Ilham UnggaraUniversitas Gadjah Mada1
Yessi YunitasariUniversitas Gadjah Mada1
Dias Aziz PramuditaUniversitas Gadjah Mada1
Abidah ElcholiqiUniversitas Gadjah Mada1
Hans Satria KusumaUniversitas Gadjah Mada1
Yunita SariUniversitas Gadjah Mada1
Rayhan Naufal RamadhanUniversitas Gadjah Mada1
Faisal RamadhanUniversitas Gadjah Mada1
Muhammad Bintang BahyUniversitas Gadjah Mada1
Muhammad Fawwaz MaydaUniversitas Gadjah Mada1
Aditya PermanaUniversitas Gadjah Mada1
Timothy K. ShihNational Central University1
Anastasya LatubessyMuria Kudus University1
Sri KusrohmaniahUniversitas Gadjah Mada1

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">Identification of Rice Variety Using Geometric Features and Neural Network</dc:title>
	<dc:creator>Srimulyani, Wahyu</dc:creator>
	<dc:creator>Musdholifah, Aina</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Image Processing; Rice Varieties; Geometric Feature; Learning Vector Quantization; Backpropagation</dc:subject>
	<dc:description xml:lang="en-US"> Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.</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>2019-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/48203</dc:identifier>
	<dc:identifier>10.22146/ijccs.48203</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 3 (2019): July; 301-312</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/48203/25089</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>
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