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All Journal Tadulako Journal Sport Sciences And Physical Education Publikasi Pendidikan Students´ Journal of Economic and Management Intelektualita Monex: Journal of Accounting Research Jurnal Pengabdian UntukMu NegeRI Jurnal Basicedu JURTEKSI Jurnal Teknologi Terapan Jurnal Review Pendidikan dan Pengajaran (JRPP) EVOLUSI : Jurnal Sains dan Manajemen Jurnal Kejaora (Kesehatan Jasmani dan Olahraga) JURNAL ILMIAH OBSGIN : Jurnal Ilmiah Ilmu Kebidanan & Kandungan JURNAL MEDICAL JURNAL ILMIAH FEASIBLE (JIF) Judika (Jurnal Nusantara Medika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Ilmiah Kesehatan Rustida Indonesia Berdaya Sentra Cendekia JURNAL ABDIMAS TRIDHARMA MANAJEMEN Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pengabdian kepada Masyarakat Dynamic Management Journal Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal PKM Manajemen Bisnis Jurnal Keperawatan Bulletin of Pedagogical Research NUSRA: Jurnal Penelitian dan Ilmu Pendidikan Proceedings Series on Physical & Formal Sciences MAUIZHAH JURNAL KAJIAN KEISLAMAN Journal Of World Science AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis Informatics and Computer Engineering Journal Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An International Journal of Islamic Business and Management Review Jurnal Cakrawala Ilmiah Indonesian Journal of Informatic Research and Software Engineering Jurnal Basicedu Jurnal Pepadu Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Jurnal Ilmu Sosial dan Humaniora Indonesian Research Journal on Education Sewagati Journal of Software Engineering and Information System (SEIS) West Science Interdisciplinary Studies International Journal of Health Sciences Jurnal Abdimas Jatibara West Science Interdisciplinary Studies Jurnal Pengabdian Masyarakat dan Riset Pendidikan Indonesian Journal of Community Empowerment Jurnal Keperawatan Profesional Konstruktivisme : Jurnal Pendidikan dan Pembelajaran ABDIMAS Iqtishadia JI-KES (Jurnal Ilmu Kesehatan)
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Journal : JURTEKSI

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TRAFFIC FLOW DETECTION USING YOLOV4 AND DEEPSORT ON NVIDIA JETSON NANO Taufiq, Reny Medikawati; Syahril, Syahril; Rafdi, Faris Abi; Firdaus, Rahmad; Sunanto, Sunanto; Muarif, Putri Fadhilla
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3871

Abstract

Abstract: This study aims to develop a Deep Learning-based Traffic Flow Detector to automatically and accurately observe traffic flow. Conventional traffic observation is often conducted manually or via CCTV, but it is prone to human error and difficult to use for real-time trend analysis. In this study, the YOLOv4 method is used to detect four types of vehicles (cars, motorcycles, buses, trucks). To continuously track vehicle movement and address occlusion issues, the Deep SORT algorithm is implemented. The YOLOv4 model used is a pre-trained model and was tested on seven CCTV video recordings obtained from the official website of the Pekanbaru City Transportation Department. The system was implemented on a limited device, the Nvidia Jetson Nano, as a simulation of direct CCTV integration. Test results showed a highest precision of 98%, but the maximum accuracy achieved was only 26%. This low accuracy is influenced by several factors, including video resolution, detection model quality, and lighting conditions. Nevertheless, the system demonstrates potential to support future traffic management and engineering decisions but still requires further optimization, including improving video resolution and quality, retraining the model with a more representative local dataset, using lighter and more accurate detection models, and optimizing the tracking algorithm. Keywords: deep learning; deepsort; NVIDIA Jetson NANO; traffic flow; YOLOv4  Abstrak: Penelitian ini bertujuan mengembangkan Traffic Flow Detector berbasis Deep Learning untuk mengobservasi arus lalu lintas secara otomatis dan akurat. Observasi lalu lintas konvensional sering dilakukan secara manual atau melalui CCTV, namun rentan terhadap human error dan sulit digunakan untuk menganalisis tren secara real-time. Pada penelitian ini digunakan metode YOLOv4 untuk mendeteksi empat jenis kendaraan (mobil, motor, bus, truk). Untuk melacak pergerakan kendaraan secara berkelanjutan dan mengatasi masalah occlusion, digunakan algoritma Deep SORT. Model YOLOv4 yang digunakan merupakan pre-trained model dan diujikan pada tujuh rekaman video CCTV yang diambil dari situs resmi Dinas Perhubungan Kota Pekanbaru. Sistem ini diimplementasikan pada perangkat terbatas Nvidia Jetson Nano sebagai simulasi penerapan langsung pada CCTV. Hasil pengujian menunjukkan presisi tertinggi mencapai 98%, namun akurasi tertingginya hanya sebesar 26%. Rendahnya akurasi dipengaruhi oleh beberapa faktor seperti resolusi video, kualitas model deteksi, serta kondisi pencahayaan. Meski demikian, sistem ini menunjukkan potensi untuk membantu pengambilan keputusan dalam manajemen dan rekayasa lalu lintas di masa depan, namun masih membutuhkan optimasi lebih lanjut, seperti  peningkatan kualitas video input, pelatihan ulang model dengan dataset lokal, penggunaan model deteksi yang lebih ringan dan akurat serta pengoptimalan algoritma pelacakan. Kata kunci: deep learning deepsort; Nvidia Jetson Nano; traffic flow; YOLOv4
TRAFFIC FLOW DETECTION USING YOLOV4 AND DEEPSORT ON NVIDIA JETSON NANO Taufiq, Reny Medikawati; Syahril, Syahril; Rafdi, Faris Abi; Firdaus, Rahmad; Sunanto, Sunanto; Muarif, Putri Fadhilla
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3871

Abstract

Abstract: This study aims to develop a Deep Learning-based Traffic Flow Detector to automatically and accurately observe traffic flow. Conventional traffic observation is often conducted manually or via CCTV, but it is prone to human error and difficult to use for real-time trend analysis. In this study, the YOLOv4 method is used to detect four types of vehicles (cars, motorcycles, buses, trucks). To continuously track vehicle movement and address occlusion issues, the Deep SORT algorithm is implemented. The YOLOv4 model used is a pre-trained model and was tested on seven CCTV video recordings obtained from the official website of the Pekanbaru City Transportation Department. The system was implemented on a limited device, the Nvidia Jetson Nano, as a simulation of direct CCTV integration. Test results showed a highest precision of 98%, but the maximum accuracy achieved was only 26%. This low accuracy is influenced by several factors, including video resolution, detection model quality, and lighting conditions. Nevertheless, the system demonstrates potential to support future traffic management and engineering decisions but still requires further optimization, including improving video resolution and quality, retraining the model with a more representative local dataset, using lighter and more accurate detection models, and optimizing the tracking algorithm. Keywords: deep learning; deepsort; NVIDIA Jetson NANO; traffic flow; YOLOv4  Abstrak: Penelitian ini bertujuan mengembangkan Traffic Flow Detector berbasis Deep Learning untuk mengobservasi arus lalu lintas secara otomatis dan akurat. Observasi lalu lintas konvensional sering dilakukan secara manual atau melalui CCTV, namun rentan terhadap human error dan sulit digunakan untuk menganalisis tren secara real-time. Pada penelitian ini digunakan metode YOLOv4 untuk mendeteksi empat jenis kendaraan (mobil, motor, bus, truk). Untuk melacak pergerakan kendaraan secara berkelanjutan dan mengatasi masalah occlusion, digunakan algoritma Deep SORT. Model YOLOv4 yang digunakan merupakan pre-trained model dan diujikan pada tujuh rekaman video CCTV yang diambil dari situs resmi Dinas Perhubungan Kota Pekanbaru. Sistem ini diimplementasikan pada perangkat terbatas Nvidia Jetson Nano sebagai simulasi penerapan langsung pada CCTV. Hasil pengujian menunjukkan presisi tertinggi mencapai 98%, namun akurasi tertingginya hanya sebesar 26%. Rendahnya akurasi dipengaruhi oleh beberapa faktor seperti resolusi video, kualitas model deteksi, serta kondisi pencahayaan. Meski demikian, sistem ini menunjukkan potensi untuk membantu pengambilan keputusan dalam manajemen dan rekayasa lalu lintas di masa depan, namun masih membutuhkan optimasi lebih lanjut, seperti  peningkatan kualitas video input, pelatihan ulang model dengan dataset lokal, penggunaan model deteksi yang lebih ringan dan akurat serta pengoptimalan algoritma pelacakan. Kata kunci: deep learning deepsort; Nvidia Jetson Nano; traffic flow; YOLOv4
Co-Authors Afib Rulyansah Afkar, Muh Tanwirul Ahmad MAULANA Ahmad Nouvel - AMIK BSI Purwokerto Ahmad Nouvel - AMIK BSI Purwokerto Ahmad Taufik Ainia, Faridah Zahrotul Akhwani Akhwani, Akhwani Al Amien, Januar Alawiyah, Saudatul Albertus Sewianto Alfiyah, Inayah Alfiyah, Zuraida Nisaul Almaniar, Siska Amran, Hasanatul Fu'adah Andini Hardiningrum Anggraini, Chelina Anggraini, Mirna Anshori, Maz Isa Aprilian, Pingkan Aprini, Hidayah Arabiatul Adawiyah, Arabiatul Arianti, Dini Arif Rahman Hakim Asep Sulaeman, Asep Asmarani, Ratih Asmaul Lutfauziah Asrianto, Rudy Ayu Pramesti, Diah Azizah, Nuryana Elia Baidarus Bambang Priyanto, Bambang Bayu Anugerah Putra BERDA ASMARA Capriat, Zelin Ferdias Decky Aditya Zulkarnaen Dede Kusmana Dedi Setiawan Deprizon, Deprizon Dewi Ningsih, Dewi Dewi, Anis Kusuma Dhian Pratiwi, Novita DIDIK PURWANTO Djazilan, M. Sukron Djazilan, Syukron Dukiyah, Dukiyah Dwi Riana Edi Ismanto Eka Wardani, Aris Faridatuljana Binti Mohd Noor Farisha Andi Baso, Farisha Andi Faural Abadi Ferry Sugara, Ferry Firdaus, Dian Nita Fitri Handayani Gusmaniarti, Gusmaniarti Hadi, Reza Khairul Hady, Hamdy Haikal, Zikrul Handayani , Erna Handayani, Erna hanifah, iis Harun Mukhtar Hasibuan, Rahmawaty Hidayati, Tutik Huda, Muchamad Samsul Huda, Muchamad Samsul Huda, Samasul Ilham Hudi Aim Abdulkarim Kokom Komalasari, Ilham Hudi Aim Abdulkarim Izzatunnisa, Nazwa johansyah johansyah Karsid, Karsid Kasiyun, Suharmono Khasanah, Uswatul Kumalasari, Shofi Eka Kusyairi, Achmad Laily Indrayanti Yusuf Laily, Izzatul Leisthari, Fepi Lestari, Novianti Kamla Lestari, Noviyanti Kartika Dwi Lisman, Muhammad maharani, krisnina Mahmudah, Faridatun Nurul Manoefris Kasim Mariani Mariani Mariati, Pance Maryani, Ina Mashuri Mashuri Mauludiya, Melynda Mikrad mikrad Mispani, Mispani Mohamad, Mohd Saberi Moyo, Kgomotso Muarif, Putri Fadhilla Muhamad Sardiman Muhammad Nur, Hidayat Muhammad Thamrin Muhammad Thamrin Hidayat Mukti, Fiqhri Nugroho Mulya, Yusha Tri Musarofah, Musarofah MUSLIMIN IBRAHIM Mustofa Mustofa Nafi'ah, Nafi'ah Nafiah, Nafiah Nafolion Nur Rahmat, Nafolion Nur Najih, Arjun Nandana, Pandu Ishaq Napitupulu, Rizal Afif A. Nashirin, Rohmatun Ng Phi Shi Nur Hamim Nurfita, Roro Nurhaini, Siti Nurismalatri, Nurismalatri Nurmin Arianto, Nurmin Nusantara, Ana Fitria Nuzul Imam Fadlilah Pannyiwi, Rahmat Praptiningtiyas, Ariska Prima Dewi Kusumawati Puji, Ari Andriyas Purba, Horas V Purnaning, Dyah Purwanto, Hadi Purwati Purwati Puspitarini, Renny Candradewi Putri, Novitya Arnika Rafdi, Faris Abi Rahayu, Dewi Widiana Rahmad Firdaus Rahmat, Rezqiqah Aulia Ramawal, Jon Rangkuti, Muhammad Al-Ikhsan Remli, Muhammad Akmal Retno Wulansari Ridhollah, Farhan Rofan Aziz Rohadi Rohadi Rohmah, Sonia Jasmine Rohmah, Syifaur Rusdiyanto, Nurholis Rusnaeni, Nani Safariningsih, Ratna Tri Hari Saifudin Saifudin Saifudin Salsabiila, Iffah Sari, Galuh Mayang Sari, Maftuhatul Ulumiyah Kumalasari Sarra, Hustna Dara Shabrina, Nina Sholihah, Nasihatus Siti Aisyah Soni Sri Hartatik Suciati Suciati Suharmanto, Ardelia Suryadi Imran, Suryadi Susanto, Rudi Umar Syahril Syahril Syamsul Ghufron Taufiq, Reny Medikawati Tika Handayani Tutik Ekasari Vadlya, Vadlya Maarif Valia, Virna Githa Veriady Purba, Jan Horas Wahyu Nur Chalamsa Wahyusari, Shinta Wan Salihin Wong, Khairul Nizar Syazwan Wardika, Wardika Wicaksono, Dedi Yesita Astarina Yogasnumurti, Raras Risia Yuly Peristiowati Zahra, Fira May Zahro, Ma’rifatul Lailatus Zuhan, Arif