DETEKSI KEPADATAN KENDARAAN MENGGUNAKAN YOLO DENGAN ALGORITMA 1-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK (1D CNN) BERDASARKAN REKAMAN CCTV DI JALAN KOTA PALEMBANG

RADISTY, ANGGUN PUTRI and Sukemi, Sukemi and Oklilas, Ahmad Fali (2025) DETEKSI KEPADATAN KENDARAAN MENGGUNAKAN YOLO DENGAN ALGORITMA 1-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK (1D CNN) BERDASARKAN REKAMAN CCTV DI JALAN KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

This Automatic vehicle density detection on major roads in Palembang City is performed by combining the YOLOv8 method and the 1- Dimensional Convolutional Neural Network (1D-CNN) algorithm. YOLOv8 detects vehicle objects, namely cars and motorcycles, from image datasets and CCTV video recordings. The detection results, in the form of vehicle counts, are used as numerical input to the 1D-CNN model to classify traffic conditions into three categories: Smooth (<30 vehicles), Moderate (30–50 vehicles), and Congested (>50 vehicles). The dataset consists of 4,500 images that have undergone preprocessing and labeling. The data is divided into three parts: 80% for training, 10% for validation, and 10% for testing. YOLOv8 training yielded a mean Average Precision (mAP) of 75.4% and an average detection accuracy of 71.54%. The 1D-CNN model achieved a classification accuracy of 91.1%. System testing using 76 CCTV videos from several intersections in Palembang resulted in an average accuracy of 93.42%. These results show that the combination of YOLOv8 and 1D-CNN is effective for real-time traffic density monitoring and supports data-driven decision-making in urban traffic management.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLOv8, 1D-CNN, kepadatan lalu lintas, kendaraan, CCTV.
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA158.7 Computer network resources Including the Internet Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150-4380 Computer network resources
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Anggun Putri Radisty
Date Deposited:	31 Jul 2025 08:39
Last Modified:	31 Jul 2025 08:39
URI:	http://repository.unsri.ac.id/id/eprint/181790

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