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

KEMUNINGSARI, RORO and Oklilas, Ahmad Fali (2023) PENENTUAN JALUR TERBAIK DENGAN MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK DAN PARTICLE SWARM OPTIMIZATION SEBAGAI PENERAPAN SMART TRANSPORTATION PADA SMART CITY. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Penentuan Jalur Terbaik, YOLOv3, Artificial Neural Network, Particle Swarm Optimization, Cheapest Insertion Heuristic
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:	Roro Kemuningsari
Date Deposited:	25 Sep 2023 06:45
Last Modified:	25 Sep 2023 06:45
URI:	http://repository.unsri.ac.id/id/eprint/129108

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