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STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 8 No. 1 (2015) · pp. 11–18

DOI
10.21609/jiki.v8i1.279
ISSN
2502-9274, 2088-7051
Published
2015-03-26
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

Keywords

Collaboration network of the authors

10 authors · 16 co-authorship links · 12 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • University of Indonesia
  • Universitas Negeri Surabaya
  • Binus University
  • University of Edinburgh
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Anwar Ma’SumUniversity of Indonesia2
Alexander Agung Santoso GunawanBinus University2
Umi MahdiyahInstitut Teknologi Sepuluh Nopember2
Tjan BasaruddinUniversity of Indonesia1
Diane FitriaUniversity of Indonesia1
Muhammad Febrian RachmadiUniversity of Edinburgh1
Zumrotun NafisahUniversitas Negeri Surabaya1

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">STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL</dc:title>
	<dc:creator>Athoillah, Muhammad</dc:creator>
	<dc:creator>Irawan, M. Isa</dc:creator>
	<dc:creator>Imah, Elly Matul</dc:creator>
	<dc:subject xml:lang="en-US">Backpropagation</dc:subject>
	<dc:subject xml:lang="en-US">Classification</dc:subject>
	<dc:subject xml:lang="en-US">Image Retrieval</dc:subject>
	<dc:subject xml:lang="en-US">K-NN</dc:subject>
	<dc:subject xml:lang="en-US">SVM</dc:subject>
	<dc:description xml:lang="en-US">Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2015-03-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/279</dc:identifier>
	<dc:identifier>10.21609/jiki.v8i1.279</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 8 No. 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 11-18</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v8i1</dc:source>
	<dc:language>eng</dc:language>
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