PENERAPAN SMART TRANSPORTATION PADA SMART CITY MENGGUNAKAN METODE HYBRID RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION UNTUK PENENTUAN JALUR TERBAIK

SANTIKA, DELLA and Oklilas, Ahmad Fali (2023) PENERAPAN SMART TRANSPORTATION PADA SMART CITY MENGGUNAKAN METODE HYBRID RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION UNTUK PENENTUAN JALUR TERBAIK. Undergraduate thesis, Sriwijaya University.

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Abstract

The increasing use of vehicles in urban areas has resulted in high levels of road congestion. The focus of this research is the application of smart transportation in smart city to find the best route using road density and mileage parameters. The accuracy of the YOLOv3 model in detecting motorbikes and cars is 75.63%, the reading accuracy is 94.68%. The random forest method can be used to determine road density conditions based on the number of cars and motorcycles, the number of lanes and the distance traveled. After that, optimization was carried out using particle swarm optimization so that there was an increase in model accuracy from 87.50% to 89.06% while reading accuracy from 86.8% to 90.28%. The heuristic star search algorithm is one of the well-known algorithms for finding the best path. From the results of the trials that have been carried out, line 1 is the best route because it has the smallest weight value due to the fact that the road conditions are mostly smooth even though it is not the route with the shortest distance, namely on 02 January 2023 in the afternoon and 05 January 2023 in the afternoon, and line 5 is the best route because it has the smallest weight value and it is the shortest distance traveled compared to other routes, namely January 02, 2023 morning, and afternoon, January 03, 2023 morning, afternoon and evening, January 04, 2023 morning, afternoon and evening and January 05, 2023 morning and afternoon.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLOv3, Random Forest, Particle Swarm Optimization, Heuristic Search, Best Path.
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q223.M517 Science -- Information services. Information storage and retrieval systems --Science.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Della Santika
Date Deposited:	26 Jul 2023 06:40
Last Modified:	10 Aug 2023 07:26
URI:	http://repository.unsri.ac.id/id/eprint/122254

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