Skripsi

PENENTUAN JALUR TERBAIK MENGGUNAKAN ALGORITMA GLOWWORM SWARM OPTIMIZATION BERDASARKAN DARI HASIL OUTPUT KONDISI KEPADATAN KENDARAAN MENGGUNAKAN 1DCNN

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Penilaian

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In the application of Artificial Intelligence to determine the best route as a means to alleviate traffic congestion in the city of Palembang, the author uses the Glowworm Swarm Optimization (GSO) algorithm. Then, to determine road density conditions based on reference tables from CCTV video recordings, the author uses the One Dimensional Convolutional Neural Network (1DCNN) algorithm. The purpose of YOLOv8 is to recognize and count the number of vehicles on the road. YOLOv8 achieved an accuracy of 83% during training and testing. In the classification and counting of vehicles, it achieved an accuracy rate of 88.33% for motorcycles, 97.71% for cars, and 100% for three-wheeled motorcycles. Then, using 1DCNN to determine road density conditions with parameters such as the number of motorcycles, number of cars, number of three-wheeled motorcycles, road width, and travel distance at each intersection, it produced a model accuracy of 93.75% and a prediction accuracy of 95.16%. Followed by the Glowworm Swarm Optimization algorithm to determine the best route using parameters of road conditions and travel distance, the result identified route 4 as having the smallest weight in all conditions, namely morning, afternoon, and evening, where the smallest weight value of the best route is 13.5.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003716	T146491	T1464912024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1464912024

Publisher

Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xvii, 82 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

518.107

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Jurusan Sistem Komputer
kepadatan kendaraan

Specific Detail Info

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Statement of Responsibility

UIN YOLA

Other version/related

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