PERBANDINGAN KEPADATAN KENDARAAN MENGGUNAKAN ALGORITMA DECISION TREE DAN RANDOM FOREST BERDASARKAN REKAMAN CCTV LALU LINTAS KOTA PALEMBANG

APRIANI, PUTRI DANTY and Oklilas, Ahmad Fali (2025) PERBANDINGAN KEPADATAN KENDARAAN MENGGUNAKAN ALGORITMA DECISION TREE DAN RANDOM FOREST BERDASARKAN REKAMAN CCTV LALU LINTAS KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

This research predicts traffic density based on CCTV footage of Palembang city traffic. It uses the YOLOv9 model to detect cars and motorcycles, achieving a training performance with an mAP 0.5 of 84.4%. Furthermore, it compares traffic density using the Decision Tree and Random Forest algorithms on Monday, Wednesday, Friday, and Saturday during the morning, afternoon, and evening. The evaluation results of the Decision Tree algorithm show a model accuracy of 92%, with a training accuracy of 96.48% and a testing accuracy of 92.18%, resulting in a 4.29% difference. Meanwhile, the Random Forest algorithm achieved a model accuracy of 89%, with a training accuracy of 98.04% and testing accuracy of 89.06%, resulting in an 8.98% difference. The prediction results of actual traffic density conditions show that the Decision Tree algorithm has an accuracy of 99.60%, while Random Forest achieved 97.22%. The Decision Tree model performs better than the Random Forest model, which tends to be more overfitting.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLOv9, Decision Tree, Random Forest, kepadatan lalu lintas
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Putri Danty Apriani
Date Deposited:	21 May 2025 05:34
Last Modified:	21 May 2025 05:34
URI:	http://repository.unsri.ac.id/id/eprint/173073

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