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All Journal Jurnal Teknik Kimia USU Paduraksa : Jurnal Teknik Sipil Universitas Warmadewa Jurnal Buana Informatika Jurnal TIMES MLJ Jurnal AKSI (Akuntansi dan Sistem Informasi) Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Antivirus : Jurnal Ilmiah Teknik Informatika bit-Tech Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan JMTS: Jurnal Mitra Teknik Sipil Jurnal Informatika dan Sistem Informasi Jurnal Manajemen Bisnis Eka Prasetya Jurnal Media dan Komunikasi Jurnal Profesi Insinyur Universitas Lampung STATIKA: Jurnal Teknik Sipil Kalbisocio: Jurnal Bisnis dan Komunikasi Jurnal Teknik Kimia USU Kata Kita: Journal of Language, Literature, and Teaching eProceedings of Engineering Jurnal Manajemen Teknologi dan Sistem Informasi Simangunsong Journal of Business Administration, Management, Economic and Accounting Mount Hope Global Economic Journal Jurnal Informatika dan Bisnis PROCEEDING INTERNATIONAL BUSINESS AND ECONOMICS CONFERENCE (IBEC) SCIENTIA JOURNAL : Jurnal Ilmiah Mahasiswa Jurnal Wana Tropika Fortunate Business Review JURNAL DATA SAINS DAN TEKNOLOGI INFORMASI (DASTIS)
Michael
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Journal : Jurnal Buana Informatika

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Identifikasi Kendaraan Beroda Menggunakan Algoritma YOLOv5 Michael
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

The importance of traffic density measurement in road planning has led to efforts in automation using object detection algorithms, particularly YOLO (You Only Look Once), which are replacing error-prone and time-consuming manual processes. However, challenges arise in dense traffic conditions, posing a challenge to vehicle detection accuracy. This research aims to compare the performance of vehicle detection between two YOLO approaches: multi-view layer detection and conventional detection, focusing on YOLOv5n, YOLOv5s, and YOLOv5m. The literature review encompasses Computer Vision, YOLO implementation, and related research to provide conceptual context. The research method details the steps of vehicle identification using YOLOv5, and the evaluation includes the performance of various YOLO variants and multi-view detection approaches. Thus, this study is expected to gain deeper insights into building an effective model and facilitating the selection of a suitable YOLO model for vehicle detection.
Co-Authors Aditya Rachman Zarkasih Agustina, Shela Aleksander Purba Andry Yuliyanto Angeliou, Ryan Asriandi Ekaputra, Reza Auroka, Enjelica Ayu Dwiyana, Putri Beckham, Felix Butar Butar , Mangasi Cahyo Agung Saputra Charles Dalimunthe, Nisaul Fadilah Daniel Susilo Delliana, Santi Dharma, Ernest Kusuma Edi Wijaya edy evyanto, winda Feriani Astuti Tarigan Frans Dermanto Hutabarat Galih Rio Prayogi Gusrio Tendra Helga Meilany Henny Putri Saking Wijaya, Henny Putri Saking Humdiana Roelly Husaein, Ahmad Ig. Prasetya Dwi Wibawa Indri Rahmadhani Fitriana Ivonie Jerry Heikal Khanza, Ayu Kamila Kozen, Vincent Lie, Eric Lukamnul Hakim M Gilang Indra Mardika Makaba, Kharisma Austin Mareta, Arya melindawati Meta Kallista Michelle Mikhael Stefanus Filemon Simatupang Muhammad Awaluddin Muhammad Thoriq Al Fath Nadi, Muhammad Abi Berkah Princes, Elfindah Purba, Daniel Bungaran Purwadi Rahmandhani Fitriana, Indri Rasji, Rasji Rianti Agustini, Silvia Robinhot Gultom, Robinhot Ruslin Samosir, Rosma Natalia Selly Setiawan, Preza Sherlyna Sidabutar, Rivaldi Sri Rezeki Sugeng Wahyudiono, Sugeng Suhartati, Tatik Sutarno Thiodorus Marvin Tjandra William Widjaja Yuwono Marta Dinata