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Open access Jurnal SINTA 2

Classification of Traffic Vehicle Density Using Deep Learning

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 1 (2020) · pp. 69–80

DOI
10.22146/ijccs.50376
ISSN
2460-7258, 1978-1520
Published
2020-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data.

Keywords

Collaboration network of the authors

40 authors · 58 co-authorship links · 20 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
  • Universitas Stikubank
  • University of Indonesia
  • State University of Semarang
  • Universitas Pendidikan Ganesha
  • State University of Medan
  • Duta Wacana Christian 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
Edi WinarkoUniversitas Gadjah Mada3
Muhammad Dzulfikar FauziUniversitas Gadjah Mada2
Retantyo WardoyoUniversitas Gadjah Mada2
Sri T. HartatiUniversitas Gadjah Mada2
Edy WinarnoUniversitas Stikubank2
Aniati Murni ArymurthyUniversity of Indonesia2
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
Kharis SyabanUniversitas Gadjah Mada1
Pawit RiantoAffiliation unknown1
Adri PriadanaUniversitas Gadjah Mada1
Ade NurhopipahUniversitas Amikom Purwokerto1
Aina MusdholifahUniversitas Gadjah Mada1
Nelson RumuiPoliteknik Negeri Ketapang1
Chasandra PuspitasariUniversitas Gadjah Mada1
Kasliono KaslionoUniversitas Gadjah Mada1
Wahyu Sri MulyaniUniversitas Gadjah Mada1
Laksono KurnianggoroUniversity of Ulsan1

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">Classification of Traffic Vehicle Density Using Deep Learning</dc:title>
	<dc:creator>Kholik, Abdul</dc:creator>
	<dc:creator>Harjoko, Agus</dc:creator>
	<dc:creator>Wahyono, Wahyono</dc:creator>
	<dc:subject xml:lang="en-US">Computer vision</dc:subject>
	<dc:subject xml:lang="en-US">Complexity Vehicle density; Deep learning; Classification; Convolutional neural network</dc:subject>
	<dc:description xml:lang="en-US">The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data.</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>2020-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/50376</dc:identifier>
	<dc:identifier>10.22146/ijccs.50376</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 1 (2020): January; 69-80</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/50376/26961</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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>