ANALISIS SENTIMEN TERHADAP KEMACETAN LALU LINTAS MENGGUNAKAN YOLOV8 DENGAN ALGORITMA RECURRENT NEURAL NETWORK (RNN) BERDASARKAN DATA PADA SOSIAL MEDIA DAN REKAMAN CCTV DI JALAN PROTOKOL PALEMBANG

FRANDESCA, AGIL ANJAS and Oklilas, Ahmad Fali (2025) ANALISIS SENTIMEN TERHADAP KEMACETAN LALU LINTAS MENGGUNAKAN YOLOV8 DENGAN ALGORITMA RECURRENT NEURAL NETWORK (RNN) BERDASARKAN DATA PADA SOSIAL MEDIA DAN REKAMAN CCTV DI JALAN PROTOKOL PALEMBANG. Undergraduate thesis, Sriwijaya University.

Preview	Image RAMA_56201_09011282025087_Cover.jpg - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (707kB) | Preview
	Text RAMA_56021_09011282025087.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_56201_09011282025087_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (9MB) | Request a copy
	Text RAMA_56201_09011282025087_0015107201_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_56201_09011282025087_0015107201_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (627kB) | Request a copy
	Text RAMA_56201_09011282025087_0015107201_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (804kB) | Request a copy
	Text RAMA_56201_09011282025087_0015107201_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011282025087_0015107201_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (102kB) | Request a copy
	Text RAMA_56201_09011282025087_0015107201_06_ref.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (162kB) | Request a copy
	Text RAMA_56201_09011282025087_0015107201_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (794kB) | Request a copy

Abstract

This research aims to analyse public sentiment towards traffic congestion in Palembang City using YOLOv8 model and Recurrent Neural Network (RNN) algorithm. The background of this research focuses on the importance of accurate understanding of traffic conditions, which can be obtained from social media data and video recordings. The methods used include data collection from social media platforms and videos about traffic jams, followed by analysis using object detection and sentiment classification techniques. The evaluation results of the Recurrent Neural Network (RNN) algorithm performed quite well in analysing social media sentiment, with an accuracy rate of 91% on 162 lines of training data and 80% on 66 lines of test data. Of the total 66 lines of data analysed, 15 data matches were found between the Social Media test data and the video recording data, resulting in an accuracy rate of 22.73%. This shows the low level of public trust in social media.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentiment, Kemacetan Lalu Lintas, YOLOv8, Recurrent Neural Network, RNN, Media Sosial, Rekaman Video.
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:	Agil Anjas Frandesca
Date Deposited:	15 May 2025 04:35
Last Modified:	15 May 2025 04:35
URI:	http://repository.unsri.ac.id/id/eprint/167658

Actions (login required)

View Item