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Jurnal SINTA 2

MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 13 No. 2 (2020) · pp. 47–55

DOI
10.21609/jiki.v13i2.808
ISSN
2502-9274, 2088-7051
Published
2020-07-01
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods.

Keywords

Collaboration network of the authors

40 authors · 72 co-authorship links · 13 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.

  • Institut Teknologi Sepuluh Nopember
  • University of Brawijaya
  • Universitas Airlangga
  • Hasanuddin University
  • University of Indonesia
  • Universitas Muhammadiyah Malang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nanik SuciatiInstitut Teknologi Sepuluh Nopember9
Diana PurwitasariInstitut Teknologi Sepuluh Nopember8
Handayani TjandrasaInstitut Teknologi Sepuluh Nopember7
Siti RochimahInstitut Teknologi Sepuluh Nopember4
Anny YuniartiInstitut Teknologi Sepuluh Nopember3
Hari GinardiInstitut Teknologi Sepuluh Nopember2
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember2
Hilya TsaniyaInstitut Teknologi Sepuluh Nopember2
Hendra MesraHasanuddin University2
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember2
Eha Renwi AstutiUniversitas Airlangga2
Ramadhan Hardani PutraUniversitas Airlangga2
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
M. Rahmat WidyantoUniversity of Indonesia1
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Ratih Kartika DewiUniversity of Brawijaya1
Yudhi PurwanantoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiAffiliation unknown1
Faida RoyaniAffiliation unknown1
Dewi RosidaAffiliation unknown1
Evy Kamilah RatnasariInstitut Teknologi Sepuluh Nopember1
Nur HayatinUniversitas Muhammadiyah Malang1
Mustika MentariInstitut Teknologi Sepuluh Nopember1
Rizal Setya PerdanaUniversity of Brawijaya1
Fahrur RoziInstitut Teknologi Sepuluh Nopember1
Ahmad Hayam BrilianAffiliation unknown1
Aditya BagusmulyaInstitut Teknologi Sepuluh Nopember1
Mirza Galih KurniawanInstitut Teknologi Sepuluh Nopember1
Christian Sri Kusuma AdityaInstitut Teknologi Sepuluh Nopember1
Mamluatul Hani’ahAffiliation unknown1
Aryo HartoInstitut Teknologi Sepuluh Nopember1
Yunan Helmi MahendraInstitut Teknologi Sepuluh Nopember1
Nursanti Novi ArisaInstitut Teknologi Sepuluh Nopember1
Mohammad SholikInstitut Teknologi Sepuluh Nopember1
Martini Dwi Endah SusantiInstitut Teknologi Sepuluh Nopember1
Fadli Husein WattiheluwInstitut Teknologi Sepuluh Nopember1

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">MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV</dc:title>
	<dc:creator>Tanuwijaya, Evan</dc:creator>
	<dc:creator>Fatichah, Chastine</dc:creator>
	<dc:subject xml:lang="en-US">Detection of car parking availability</dc:subject>
	<dc:subject xml:lang="en-US">CNN</dc:subject>
	<dc:subject xml:lang="en-US">YOLO V3</dc:subject>
	<dc:description xml:lang="en-US">The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2020-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/808</dc:identifier>
	<dc:identifier>10.21609/jiki.v13i2.808</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 13 No. 2 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 47-55</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v13i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/808/428</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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