PERBANDINGAN ALGORITMA GENETIK TERHADAP PARTICLE SWARM OPTIMIZATION DALAM MENENTUKAN RUTE TERBAIK PADA SISTEM TRANSPORTASI DI JALAN RAYA KOTA PALEMBANG

PRIYANTI, ANA EMILIA and Oklilas, Ahmad Fali (2025) PERBANDINGAN ALGORITMA GENETIK TERHADAP PARTICLE SWARM OPTIMIZATION DALAM MENENTUKAN RUTE TERBAIK PADA SISTEM TRANSPORTASI DI JALAN RAYA KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

This research focuses on identifying the optimal route by analyzing the results of Genetic Algorithm and Particle Swarm Optimization. This research uses You Only Look Once version 9 (YOLOv9) to create a vehicle detection and counting system using CCTV footage, with the results of Average Precision (mAP) values of 84.4% during training and 83.9% during testing. Furthermore, this study used the Decision Tree output from previous research to categorize road conditions as smooth, moderate, or congested through various parameters, including the number of vehicles, road width, and travel distance. In estimating road congestion, the Decision Tree produced a model accuracy of 92%. Next, Genetic and Particle Swarm Optimization algorithms were used to determine the best route based on travel distance and road conditions. The analysis showed that route 4 was frequently selected in various scenarios due to its minimal weight. Route 3 emerged as the preferred option on Wednesday afternoons, Saturday afternoons and evenings, while route 1 was preferred on Friday and Saturday mornings due to its shorter distance. However, route 1 was not consistently the top choice as it crosses the intersection of Charitas and Polda which often experiences congestion. A comparison between Particle Swarm Optimization and Genetic Algorithm shows that both produce almost the same routes and weights, although Particle Swarm Optimization runs faster with an average time difference of 0.0420 seconds.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Rute Terbaik, YOLOv9, Roboflow, Genetik, Particle Swarm Optimization
Subjects:	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:	Ana Emilia Priyanti
Date Deposited:	21 May 2025 05:46
Last Modified:	21 May 2025 05:46
URI:	http://repository.unsri.ac.id/id/eprint/173469

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