Image of PENENTUAN JALUR TERBAIK DENGAN MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK DAN PARTICLE SWARM OPTIMIZATION SEBAGAI PENERAPAN SMART TRANSPORTATION PADA SMART CITY

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PENENTUAN JALUR TERBAIK DENGAN MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK DAN PARTICLE SWARM OPTIMIZATION SEBAGAI PENERAPAN SMART TRANSPORTATION PADA SMART CITY

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Traffic congestion in Indonesia remains a complex and unresolved issue. With the advancement of Artificial Intelligence (AI), Machine Learning, and Optimization, there has been a recent innovation to address this traffic problem. This innovation involves determining optimal routes by harnessing these advanced technologies. Artificial Intelligence (AI), Machine Learning, and Optimization technologies are used to analyze traffic patterns, forecast road conditions, and identify congestion patterns. The aim of this research is to find the best routes by combining YOLOv3 for object detection and Artificial Neural Network (ANN) for classifying road density. Additionally, optimization using Particle Swarm Optimization (PSO) is applied for more accurate results. The Cheapest Insertion Heuristic method is also used to find optimal routes, considering factors such as distance, road width, and road conditions. The research results show that the combination of PSO-optimized ANN achieves an accuracy of 89.06%, which increases to 90.62% after optimization. Initially, the accuracy of the ANN model reaches 97.915%, and after PSO optimization, it reaches 100%. This success indicates the achievement of optimal accuracy in determining the best routes. The Cheapest Insertion Heuristic method is employed to determine optimal routes based on existing factors.

Availability
Inventory Code Barcode Call Number Location Status
2307005824T129108T1291082023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1291082023
Publisher
Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvii, 111 hlm.; Ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.650 7
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Jurusan Sistem Komputer
Jaringan Komunikasi Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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