Skripsi

DETEKSI PELANGGARAN MELAWAN ARUS LALU LINTAS MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE PADA VIDEO LALU LINTAS DI JALAN KOTA PALEMBANG

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Penilaian

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This research aims to develop an anti-traffic violation detection system by utilizing a combination of YOLOV8 algorithm used to detect vehicles from traffic video footage and SVM used to classify the violation level into three categories: low, medium, and high. The dataset used consists of 7,412 vehicle images for the YOLO model. For SVM, a reference table of 160 rows will be provided as training data and 100 video recordings of violations will be processed where 70% will be the test data and 30% will be the validation data. The training results of the YOLOv8 model show an accuracy of 88.76% for training data, 88.67% for validation, and 86% for testing. Meanwhile, the SVM model produces 93% accuracy on validation data (30 samples) and 86% on testing data (70 samples). Based on the classification results, as many as 66% of violations in Palembang City are classified into the low category, with the most violations committed by motorcyclists.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004409	T180020	T1800202025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1800202025

Publisher

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

Collation

xxiii, 443 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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