KLASIFIKASI GOLONGAN KENDARAAN DI JALAN TOL MENGGUNAKAN METODE DEEP LEARNING BERBASIS CITRA BERGERAK

LAZUARDI, MUHAMMAD and Fachrurrozi, Muhammad (2023) KLASIFIKASI GOLONGAN KENDARAAN DI JALAN TOL MENGGUNAKAN METODE DEEP LEARNING BERBASIS CITRA BERGERAK. Undergraduate thesis, Sriwijaya University.

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Abstract

Motorized vehicles operating on toll roads play an important role in the mobility of modern society. Efficient traffic monitoring and management is an important aspect in maintaining the safety and comfort of toll road users. The research proposes the use of the YOLOv7 (You Only Look Once version 7) method to classify vehicle groups operating on toll roads. Moving images were chosen as input to the program developed by the researcher, which will also be the program output format where the program output has been given a bounding box for vehicles that have been successfully detected and classified. The program built uses 3 combinations of Epoch and batch size, namely Batch 16 Epoch 55, Batch 16 Epoch 250, and Batch 16 Epoch 400 to obtain the feature model for the testing process. The results of the testing process obtained Precision values of 0.963, Recall 0.976, and Accuracy 0.953. This system has great potential for the development of technological progress and progress in toll traffic in Indonesia.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification of Vehicle Classes, deep learning, YOLOv7, Video
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Lazuardi
Date Deposited:	23 Nov 2023 01:18
Last Modified:	23 Nov 2023 01:18
URI:	http://repository.unsri.ac.id/id/eprint/130902

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