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All Journal PROCEEDING IC-ITECHS 2014 Jurnal Inotera Compiler Simtek : Jurnal Sistem Informasi dan Teknik Komputer CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Jurnal ICT : Information Communication & Technology Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika SmartComp Informasi interaktif : jurnal informatika dan teknologi informasi
Pamekas, Bondan Wahyu
Universitas Duta Bangsa Surakarta
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : SKANIKA: Sistem Komputer dan Teknik Informatika

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PENGEMBANGAN SISTEM PERHITUNGAN JUMLAH KENDARAAN BERDASARKAN JENIS KENDARAAN MENGGUNAKAN ALGORITMA YOLO SECARA REALTIME Kusuma, Adri Surya; Pradana, Afu Ichsan; Pamekas, Bondan Wahyu
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 2 (2024): Jurnal SKANIKA Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i2.3201

Abstract

This study developed a vehicle counting system based on vehicle types using the YOLO algorithm in real-time. With the advancement of artificial intelligence technology, particularly in computer vision, outdoor media has also advanced through the computation of CCTV image results installed on such media. The significance of this research is found in its ability to provide accurate and real-time traffic data that can be utilized by local governments, traffic management companies, property developers, and advertising companies for traffic planning, security, and marketing analysis. The research method involved training the YOLOv9 model and centroid-based tracking on a dataset that included four vehicle classes: car, motorcycle, bus, and truck. The results showed that the developed system could detect and track vehicles with high accuracy, achieving a mAP of 0.9, precision of 0.909, recall of 0.828, and an F1-Score of 0.867. However, the system's performance heavily depends on the hardware specifications used, and the detection for motorcycles has lower evaluation scores compared to other vehicle types. This study indicates that improving hardware specifications and optimizing the model can enhance system performance. These findings are important as they reveal the considerable potential of using the YOLO algorithm in real-time traffic monitoring applications.
Co-Authors Arif Budiman Arif Budiman Dinia Ilmawati Eko Purwanto Emha Taufiq Luthfii Faulinda Ely Nastiti Harjanto, Prabandaru Pramu Ikhsani, Putri Amalia Indraswari, Elsa Fachrisa Karima, Yuyun Kusrini Kusrini Kusrini Kusrini Kusuma, Adri Surya Maulindar, Joni Muhammad, Nibras Fa’iq Musthofa Galih Pradana Nedy, Yohanes Hans Nickhen Sindyana Susilo Novianto, Novianto Nur Auliya Saleh, Muhammad Daivany Nurchim Nurchim Pradana, Afu Ichsan Pratama, Wahyu Adi Pujo Hari Saputro Putri, Fanisa Halya Wardana, Fhatoni Nur Warihaji, Wijasena Widagdo, Nanda Ari Wahyu Winasis, Afiat Lintang Yohanes Hans Nedy