Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002552	T167658	T1676582025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1676582025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, 138 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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