KLASIFIKASI KEPADATAN KENDARAAN MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DI JALAN PROTOKOL KOTA PALEMBANG

ZUHDI, RIFQI and Oklilas, Ahmad Fali (2025) KLASIFIKASI KEPADATAN KENDARAAN MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DI JALAN PROTOKOL KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

Traffic congestion problems in major cities, including Palembang, require an effective and accurate vehicle density classification system. This study aims to classify vehicle density on the main roads of Palembang using the Logistic Regression algorithm. The data used was obtained from vehicle detection results, such as cars, motorcycles, and three-wheeled motorcycles, using the YOLOv8 model, which achieved an accuracy of 88.51% on the training data, 88.13% on the validation data, and 88.13% on the testing data. Next, the Logistic Regression algorithm was applied to classify vehicle density levels into three categories: smooth, moderate, and congested, with an accuracy rate of 87.50%. However, when compared with manual predictions on 19 video recordings, the accuracy decreased to 84.21% due to discrepancies in 3 data points. The results of this study indicate that this method is sufficiently reliable to support decision-making related to traffic management in urban areas.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning 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:	Rifqi Zuhdi
Date Deposited:	07 Aug 2025 05:13
Last Modified:	07 Aug 2025 05:13
URI:	http://repository.unsri.ac.id/id/eprint/179873

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