Skripsi

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

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Penilaian

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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 (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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005249	T181790	T1817902025	Central Library (References)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1817902025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xxii, 549 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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