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ANALISIS SENTIMEN TERHADAP KEMACETAN LALU LINTAS MENGGUNAKAN ALGORITMA RANDOM FOREST BERDASARKAN DATA PADA MEDIA SOSIAL DAN REKAMAN CCTV DI JALAN PROTOKOL PALEMBANG
This research aims to analyze public sentiment towards traffic congestion in Palembang City using Random Forest algorithm then video recording using YOLOv8 followed by Random Forest. The method used includes data collection from Social Media platforms and videos about traffic jams, followed by analysis using object detection techniques and sentiment classification. Calculation of video recordings obtained an accuracy result of 89.31% for motorcycles, 87.01% for cars, 100% for tricycles with an average value in the video truth table of 92.10%. The evaluation results of the Random Forest algorithm work quite well in analyzing Social Media sentiment, with an accuracy rate of 90.90% on training data and 83.58% on test data, From a total of 66 rows of data analyzed, 14 data were found that matched or matched between Random Forest 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.
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