Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003651	T145848	T1458482024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1458482024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiv, 70 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

629.040 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Analisis sentimen
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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