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PARTICLE SWARM OPTIMIZATION (PSO) FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN)

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 9 No. 1 (2016) · pp. 52–58

DOI
10.21609/jiki.v9i1.366
ISSN
2502-9274, 2088-7051
Published
2016-02-15
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

Keywords

Collaboration network of the authors

36 authors · 89 co-authorship links · 47 across different organisations

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

  • University of Indonesia
  • Politeknik Negeri Malang
  • Diponegoro University
  • Universitas Negeri Surabaya
  • University of Edinburgh
  • Tarumanagara University
  • Pelita Harapan University
  • 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
Muhammad Syaeful FajarUniversity of Indonesia2
Fadhlurrahman AfifUniversity of Indonesia2
Muhammad Anwar Ma’SumUniversity of Indonesia2
Novian HabibieUniversity of Indonesia2
Machmud Roby AlhamidiUniversity of Indonesia2
BudiantoUniversity of Indonesia1
LasguidoUniversity of Indonesia1
Fahry FathurrahmanUniversity of Indonesia1
Ratna RustamadjiUniversity of Indonesia1
Miesje Karmiati PurwanegaraUniversity of Indonesia1
Sani Muhammad IsaTarumanagara University1
Benny HardjonoPelita Harapan University1
Sumarsih Condroayu PurbaraniUniversity of Indonesia1
Qurrotin Aâ€TMyuninaUniversity of Indonesia1
Muhammad Anwar Maâ€TMsumUniversity of Indonesia1
Ahmad ArinaldiUniversity of Indonesia1
Dwi Teguh PriyantiniUniversity of Indonesia1
Ari WibisonoUniversity of Indonesia1
Andreas FebrianUtah State University1
Dewa Made Sri ArsaUniversity of Indonesia1
Grafika JatiUniversity of Indonesia1
Agung SantosoUniversity of Indonesia1
Rafli FilanoUniversity of Indonesia1
Nurul HanifahUniversity of Indonesia1
Indra WaspadaDiponegoro University1
Noel Segura MerazNagoya University1
Elly Matul ImahUniversitas Negeri Surabaya1
Zumrotun NafisahUniversitas Negeri Surabaya1
Arwin Datumaya Wahyudi SumariPoliteknik Negeri Malang1
Putri Indah MawarniPoliteknik Negeri Malang1
Kusworo AdiDiponegoro University1
Naufal BudimanAffiliation unknown1

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">PARTICLE SWARM OPTIMIZATION (PSO) FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN)</dc:title>
	<dc:creator>Syulistyo, Arie Rachmad</dc:creator>
	<dc:creator>Jati Purnomo, Dwi Marhaendro</dc:creator>
	<dc:creator>Rachmadi, Muhammad Febrian</dc:creator>
	<dc:creator>Wibowo, Adi</dc:creator>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">convolutional neural network</dc:subject>
	<dc:subject xml:lang="en-US">particle swarm optimization</dc:subject>
	<dc:subject xml:lang="en-US">deep belief network</dc:subject>
	<dc:description xml:lang="en-US">Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.  </dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2016-02-15</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/366</dc:identifier>
	<dc:identifier>10.21609/jiki.v9i1.366</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 9 No. 1 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 52-58</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v9i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/366/237</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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