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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002866	T173073	T1730732025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1730732025

Publisher

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

Collation

vii, 65 hlm.; ill.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

EM

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