PENENTUAN TINGKAT KEPADATAN LALU LINTAS KENDARAAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK DAN PENCARIAN RUTE TERBAIK MENGGUNAKAN ALGORITMA BEST FIRST SEARCH PADA JALAN PROTOKOL KOTA PALEMBANG

NUGRAHA, ADITYA PUTRA and Oklilas, Ahmad Fali (2024) PENENTUAN TINGKAT KEPADATAN LALU LINTAS KENDARAAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK DAN PENCARIAN RUTE TERBAIK MENGGUNAKAN ALGORITMA BEST FIRST SEARCH PADA JALAN PROTOKOL KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

Palembang City has become highly congested, especially in terms of traffic flow. Consequently, various traffic problems arise in Palembang City, one of which is frequent traffic jams. Hence, a study was conducted using various models and algorithms in the context of object recognition, vehicle counting, traffic density prediction, and optimal route finding. Firstly, the YOLOv8 model was developed using 3592 image data files to recognize five object classes with a training accuracy of 95% and testing accuracy of 93.83%. Furthermore, the use of YOLOv8 and DeepSORT in counting vehicles from 72 video files over 4 days showed an average accuracy of 97.99% for motorcycles and 96.38% for cars. The RNN model was then applied to classify road conditions with a training accuracy of 94.14% and testing accuracy of 93.75%. Traffic density prediction using RNN achieved an accuracy of 73.61%, while the best route search using the BFS algorithm from Ampera Bridge to Sultan Mahmud Badaruddin II Airport yielded the best route weight values and names depending on the given road conditions.

Item Type:	Thesis (Undergraduate)
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Aditya Putra Nugraha
Date Deposited:	15 Jul 2024 04:25
Last Modified:	15 Jul 2024 04:25
URI:	http://repository.unsri.ac.id/id/eprint/150765

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