PERBANDINGAN ANALISIS SENTIMEN KOMENTAR MEDIA SOSIAL DAN DATA ETLE DALAM KLASIFIKASI KONDISI LALU LINTAS DI KOTA PALEMBANG MENGGUNAKAN ALGORITMA NAIVE BAYES

AHNAF, HAURA and Oklilas, Ahmad Fali (2025) PERBANDINGAN ANALISIS SENTIMEN KOMENTAR MEDIA SOSIAL DAN DATA ETLE DALAM KLASIFIKASI KONDISI LALU LINTAS DI KOTA PALEMBANG MENGGUNAKAN ALGORITMA NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

Traffic management in major cities like Palembang faces congestion challenges, especially on main roads. The implementation of ETLE provides real-time data on traffic conditions, including vehicle counts, to assist in traffic flow management. Meanwhile, social media serves as an alternative source of information through user reports on congestion and accidents. This study aims to compare data from social media with traffic data from ETLE using the Naïve Bayes algorithm. The results show that the training accuracy for social media data reached 97,99%, with validation accuracy of 97,21%, and testing accuracy of 95,77%. Meanwhile, for ETLE data, the training accuracy was 84,4%, with validation accuracy of 81.6%, and testing accuracy of 95,17%. The classification results indicate a similarity percentage of 77.93% between the two datasets. Additionally, the study found that 50,8% of the data from social media expressed negative sentiment toward traffic conditions. Future research is expected to use a more balanced dataset to avoid data imbalance and consider employing more advanced algorithms to address traffic issues more comprehensively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Naïve Bayes, Sentiment Analysis, Traffic, ETLE, Palembang City
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:	Haura Ahnaf
Date Deposited:	26 Mar 2025 07:24
Last Modified:	26 Mar 2025 07:24
URI:	http://repository.unsri.ac.id/id/eprint/160100

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