Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas

., Sutrisno; Cholissodin, Imam; Christanti, Rina; Dewi, Candra; Hidayat, Nurul

doi:10.25126/jtiik.201521132

Jurnal SINTA 2

Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 2 No. 1 (2015) · pp. 67–72

DOI: 10.25126/jtiik.201521132
ISSN: 2528-6579, 2355-7699
Published: 2015-02-17
Publisher: Fakultas Ilmu Komputer, Universitas Brawijaya
Language: en

Abstract

AbstrakPenggunaan citra digital untuk keperluan penelitian sudah banyak dilakukan, salah satunya yaitu segmentasi. Segmentasi berfungsi untuk mendeteksi objek - objek yang terdapat pada citra, sehingga hasil segmentasi sangat penting untuk proses selanjutnya. Pada penelitian ini diusulkan teknik optimasi hasil background subtraction menggunakan kombinasi frame difference (FD) atau difference image dengan filter SDGD dan running average (RA) atau background updating dengan filter SDGD untuk diterapkan pada blob analysis. Alasan utama menggunakan penggabungan kedua metode tersebut adalah karena seringnya terdapat piksel objek yang tidak mampu dideteksi sehingga akan mengurangi tingkat optimasi pengenalan objek. Hasil pengujian akurasi dari 10 data uji yang masing – masing terdiri dari 30 frame menunjukkan bahwa aplikasi ini memiliki nilai akurasi tertinggi yakni 90% untuk pengujian threshold dan 100% untuk pengujian ukuran structure element. Sehingga dapat disimpulkan bahwa aplikasi ini mampu melakukan segmentasi kendaraan dengan baik.Kata kunci: filter SDGD, blob analysis, video lalu lintas, background subtraction.AbstractThe use of digital images for the purposes of research has been often applied, one of them is segmentation. Segmentation is used to detect objects contained in the image, so the segmentation result is very important for further processing. In this study, the results of the optimization technique proposed background subtraction using a combination of frame difference (FD) or a difference image with filter SDGD and running average (RA) or background updating with SDGD filter to be applied blob analysis. The main reason to use the merger of these two methods is that often there are pixels that are not able to detect objects that will reduce the level of optimization object recognition. The results of accuracy testing using 10 data testing for each data consisting of 30 frames shows that the system proposed in this paper has best accuracy of 90% for testing the threshold and 100% for testing the size of structure element. So it can be concluded that this system capable to segmentation the vehicle properly.Keywords: filter SDGD, blob analysis, traffic video, background subtraction

Collaboration network of the authors

40 authors · 108 co-authorship links · 3 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.

University of Brawijaya
Universitas Amikom Purwokerto
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
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Show as table

Co-author	Organisation	Joint articles
Arief Andy Soebroto	University of Brawijaya	4
Daneswara Jauhari	University of Brawijaya	4
Dhimas Anjar Prabowo	University of Brawijaya	3
Ahmad Afif Supianto	University of Brawijaya	3
Canny Amerilyse Caesar	University of Brawijaya	2
Latifah Hanum	University of Brawijaya	2
Muhammad Fhadli	University of Brawijaya	2
Anang Hanafi	University of Brawijaya	2
Listiya Surtiningsih	University of Brawijaya	2
Fitra Abdurrachman Bachtiar	University of Brawijaya	2
Indriati Indriati	University of Brawijaya	2
Bayu Rahayudi	University of Brawijaya	2
Dany Primanita Kartikasari	University of Brawijaya	1
Yusi Tyroni Mursityo	University of Brawijaya	1
Randy Cahya Wihandika	University of Brawijaya	1
Maria Tenika Frestantiya	University of Brawijaya	1
Ziya El Arief	University of Brawijaya	1
Muh. Arif Rahman	University of Brawijaya	1
Alfian Himawan	University of Brawijaya	1
Budi Darma Setiawan	University of Brawijaya	1
Rekyan Regasari Mardi Putri	University of Brawijaya	1
Lailil Muflikhah	University of Brawijaya	1
Muhammad Syafiq	University of Brawijaya	1
Achmad Jafar Al Kadafi	University of Brawijaya	1
Rizka Husnun Zakiyyah	University of Brawijaya	1
Wanda Athira Luqyana	University of Brawijaya	1
Uswatun Hasanah	Universitas Amikom Purwokerto	1
Lintang Resita Mayangsari	University of Brawijaya	1
Andhica Pratama	University of Brawijaya	1
Mochammad Ainun Najib	University of Brawijaya	1
Handika Agus Fauzi	University of Brawijaya	1
Sutrisno Sutrisno	University of Brawijaya	1
Aryeswara Sunaryo	University of Brawijaya	1
Maryamah .	University of Brawijaya	1
Rizal Setya Perdana	University of Brawijaya	1

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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="id">Segmentasi Kendaraan Menggunakan Improve Blob Analysis (BA) Pada Video Lalu Lintas</dc:title>
	<dc:creator xml:lang="en">., Sutrisno</dc:creator>
	<dc:creator xml:lang="en">Cholissodin, Imam</dc:creator>
	<dc:creator xml:lang="en">Christanti, Rina</dc:creator>
	<dc:creator xml:lang="en">Dewi, Candra</dc:creator>
	<dc:creator xml:lang="en">Hidayat, Nurul</dc:creator>
	<dc:description xml:lang="id">AbstrakPenggunaan citra digital untuk keperluan penelitian sudah banyak dilakukan, salah satunya yaitu segmentasi. Segmentasi berfungsi untuk mendeteksi objek - objek yang terdapat pada citra, sehingga hasil segmentasi sangat penting untuk proses selanjutnya. Pada penelitian ini diusulkan teknik optimasi hasil background subtraction menggunakan kombinasi frame difference (FD) atau difference image dengan filter SDGD dan running average (RA) atau background updating dengan filter SDGD untuk diterapkan pada blob analysis. Alasan utama menggunakan penggabungan kedua metode tersebut adalah karena seringnya terdapat piksel objek yang tidak mampu dideteksi sehingga akan mengurangi tingkat optimasi pengenalan objek. Hasil pengujian akurasi dari 10 data uji yang masing – masing terdiri dari 30 frame menunjukkan bahwa aplikasi ini memiliki nilai akurasi tertinggi yakni 90% untuk pengujian threshold dan 100% untuk pengujian ukuran structure element. Sehingga dapat disimpulkan bahwa aplikasi ini mampu melakukan segmentasi kendaraan dengan baik.Kata kunci: filter SDGD, blob analysis, video lalu lintas, background subtraction.AbstractThe use of digital images for the purposes of research has been often applied, one of them is segmentation. Segmentation is used to detect objects contained in the image, so the segmentation result is very important for further processing. In this study, the results of the optimization technique proposed background subtraction using a combination of frame difference (FD) or a difference image with filter SDGD and running average (RA) or background updating with SDGD filter to be applied blob analysis. The main reason to use the merger of these two methods is that often there are pixels that are not able to detect objects that will reduce the level of optimization object recognition. The results of accuracy testing using 10 data testing for each data consisting of 30 frames shows that the system proposed in this paper has best accuracy of 90% for testing the threshold and 100% for testing the size of structure element. So it can be concluded that this system capable to segmentation the vehicle properly.Keywords: filter SDGD, blob analysis, traffic video, background subtraction</dc:description>
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	<dc:date>2015-02-17</dc:date>
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	<dc:identifier>10.25126/jtiik.201521132</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 2 No 1: April 2015; 67-72</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 2 No. 1: April 2015; 67-72</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201521</dc:source>
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	<dc:rights xml:lang="en">Copyright (c) 2016 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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