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PERBANDINGAN KEPADATAN KENDARAAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS DAN LONG SHORT-TERM MEMORY PADA JALAN RAYA KOTA PALEMBANG

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Penilaian

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This study aims to analyze traffic density in Palembang City by utilizing YOLOv9 for vehicle detection from CCTV recordings and employing K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) to assess density based on the detected vehicle count. The results indicate that YOLOv9 with 100 epochs achieved the best performance, with a mean Average Precision (mAP) of 0.844 for training, 0.843 for validation, and 0.839 for testing. In traffic density analysis, LSTM outperformed KNN, achieving an accuracy of 93.75% compared to KNN's 92.19%. LSTM proved to be more effective in handling sequential data and recognizing dynamic traffic patterns, whereas KNN maintained stability in balancing training and testing data but was less optimal in capturing complex traffic pattern changes.These findings suggest that the combination of YOLOv9 and LSTM offers higher accuracy, making it a strong foundation for developing AI-based systems for urban traffic monitoring and management.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002868	T173461	T1734612025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1734612025

Publisher

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

Collation

xv, 73 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

-

Edition

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Subject(s)

Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

EM

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