ANALISIS SENTIMEN TERHADAP KEMACETAN LALU LINTAS MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN) BERDASARKAN DATA PADA MEDIA SOSIAL DAN REKAMAN CCTV DI JALAN PROTOKOL KOTA PALEMBANG

NUR, MUHAMAD RAHARDI and Oklilas, Ahmad Fali (2025) ANALISIS SENTIMEN TERHADAP KEMACETAN LALU LINTAS MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN) BERDASARKAN DATA PADA MEDIA SOSIAL DAN REKAMAN CCTV DI JALAN PROTOKOL KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

This research aims to analyze public sentiment towards traffic jams in Palembang City using the K-Nearest Neighbors (KNN) algorithm and then video recordings using YOLOv8 followed by KNN. The method used includes data collection from social media platforms and videos about traffic jams, followed by analysis using object detection and sentiment classification techniques. calculation of video recordings obtained the accuracy of motorcycles by 89.31%, cars by 87.01%, three-wheeled motorcycles by 100% with the average value in the video truth table is 92.10%. The evaluation results of the KNN algorithm work quite well in analyzing Social Media sentiment, with an accuracy rate of 72.73% on training data and 71.21% on test data, From a total of 66 lines of test data analyzed, 14 data were found that matched or matched between KNN predictions and video recording data, resulting in an accuracy rate of 21.21%. Because the accuracy value is quite low, it is recommended to use other better methods, this research shows social media as an alternative source of information in monitoring traffic conditions

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	sentiment analysis, traffic jam, yolov8, k-nearest neighbots (KNN), social media, video recording
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:	Muhamad Rahardi Nur
Date Deposited:	11 Apr 2025 01:38
Last Modified:	11 Apr 2025 01:38
URI:	http://repository.unsri.ac.id/id/eprint/162689

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