Muchamad, Aditya (2025) Menghitung Durasi Lampu Lalu Lintas Menggunakan YOLOv5. Other thesis, Institut Teknologi Sepuluh Nopember.
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MUCHAMAD_ADITYA_07211840000050_BUKU_TA.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (4MB) | Request a copy |
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07211840000050-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (4MB) | Request a copy |
Abstract
Dalam penelitian ini, kami mengeksplorasi penggunaan model YOLOv5 untuk memprediksi durasi lampu lalu lintas di dalam perempatan. Dengan meningkatnya volume kendaraan dan kebutuhan untuk mengoptimalkan aliran lalu lintas, pendekatan yang efisien dalam mengelola waktu lampu lalu lintas menjadi sangat penting. Data dikumpulkan dari kamera pengawas yang terpasang di perempatan, di mana model YOLOv5 digunakan untuk mendeteksi dan mengklasifikasikan kendaraan dalam waktu nyata. Hasil dari deteksi ini kemudian dianalisis untuk menentukan durasi yang optimal bagi masing-masing fase lampu lalu lintas berdasarkan volume kendaraan yang terdeteksi. Pengujian menunjukkan bahwa metode ini dapat meningkatkan efisiensi pengaturan lalu lintas dengan mengurangi kemacetan dan waktu tunggu. Temuan ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem manajemen lalu lintas yang lebih cerdas dan responsif
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In this study, we explore the use of the YOLOv5 model to predict traffic light durations at intersections. With the increasing volume of vehicles and the need to optimize traffic flow, an efficient approach to managing traffic light timing has become crucial. Data were collected from surveillance cameras installed at intersections, where the YOLOv5 model was used to detect and classify vehicles in real time. The detection results were then analyzed to determine the optimal duration for each traffic light phase based on the detected vehicle volume. Testing showed that this method could improve traffic management efficiency by reducing congestion and wait times. These findings are expected to contribute to the development of smarter and more responsive traffic management systems
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Deteksi, Durasi Lampu Lalu Lintas, YOLOv5, Prediksi, Perempatan, Manajemen Lalu Lintas, Detect, Intersection, Managing Traffic Light, Predict, Traffic Light Dura tions |
| Subjects: | T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis |
| Depositing User: | Muchamad Aditya |
| Date Deposited: | 05 Feb 2025 01:23 |
| Last Modified: | 05 Feb 2025 01:25 |
| URI: | http://repository.its.ac.id/id/eprint/118158 |
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