ANALISIS SENTIMEN DATA INSTAGRAM TERHADAP KONDISI KEPADATAN LALU LINTAS KENDARAAN DIBANDINGKAN DENGAN DATA ETLE (ELECTRONIC TRAFFIC LAW ENFORCEMENT) DI JALAN PROTOKOL KOTA PALEMBANG MENGGUNAKAN ALGORITMA NAÏVE BAYES

SASTRI, VIJIANTIKA FAJARIA and Oklilas, Ahmad Fali (2025) ANALISIS SENTIMEN DATA INSTAGRAM TERHADAP KONDISI KEPADATAN LALU LINTAS KENDARAAN DIBANDINGKAN DENGAN DATA ETLE (ELECTRONIC TRAFFIC LAW ENFORCEMENT) DI JALAN PROTOKOL KOTA PALEMBANG MENGGUNAKAN ALGORITMA NAÏVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

Traffic congestion is a major problem in big cities like Palembang. The Electronic Traffic Law Enforcement (ETLE) system has been implemented to improve traffic discipline through surveillance based on CCTV camera technology. However, the limited coverage of ETLE indicates the need for more dynamic traffic data. In this context, social media such as Instagram with millions of active users offers a more real-time and contextual alternative data source. This research aims to analyze the sentiment of Instagram data related to traffic conditions in Palembang City using the Naïve Bayes algorithm, and compare the results with ETLE data. The results show that negative sentiment dominates people's dissatisfaction with traffic conditions in Palembang. Neutral sentiment reflects informative comments, while positive sentiment is few and generally related to improvement efforts. This research obtained classification accuracy of Instagram text dataof 94% and numeric data of 95.28%. The prediction comparison results between text data and numeric data show a 70.16% agreement.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Instagram, Analisis Sentimen, Naïve Bayes, Kota Palembang.
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:	Vijiantika Fajaria Sastri
Date Deposited:	31 Jul 2025 03:15
Last Modified:	31 Jul 2025 03:15
URI:	http://repository.unsri.ac.id/id/eprint/181842

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