PREDIKSI KEPADATAN KENDARAAN BERDASARKAN ANALISIS DATA SENTIMEN TWITTER DAN ETLE (ELECTRONIC TRAFFIC LAW ENFORCEMENT) DIRLANTAS POLDA SUMSEL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

FIKRI, MUHAMMAD and Oklilas, Ahmad Fali (2024) PREDIKSI KEPADATAN KENDARAAN BERDASARKAN ANALISIS DATA SENTIMEN TWITTER DAN ETLE (ELECTRONIC TRAFFIC LAW ENFORCEMENT) DIRLANTAS POLDA SUMSEL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

A comparison was conducted between social media data and traffic vehicle count data calculated by CCTV ETLE of Sumsel Regional Police Traffic Directorate. Both were processed and classified using the Support Vector Machine algorithm. Social media data collection was performed using scraping technique using Tweet Harvest from Playwright. Prior to classification using the Support Vector Machine algorithm, social media text data underwent preprocessing and TF-IDF method to assign weights to each word. Meanwhile, to classify vehicle counts, SVM model training was conducted on road density reference table data, which was then implemented on vehicle count data obtained from Sumsel Regional Police Traffic Directorate. After comparison of both datasets, similarity measurement was conducted, revealing that the two datasets had a similarity value of 63.89%. This was due to the differences in data characteristics and the differences in time variables between the two datasets.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Pervasive Computing
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Fikri
Date Deposited:	29 May 2024 08:51
Last Modified:	29 May 2024 08:51
URI:	http://repository.unsri.ac.id/id/eprint/145848

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