Skripsi

KLASIFIKASI CYBERBULLYING DI TWITTER MENGGUNAKAN METODE SVM (SUPPORT VECTOR MACHINE) DAN INFORMATION GAIN

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Penilaian

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Twitter, with 237.8 million daily active users, the majority aged 18-29 years, shows great potential as a communication medium. However, it also brings the problem of uncontrolled online behavior due to bullying comments. This research classifies bullying comments on Indonesian language social media using Support Vector Machine (SVM) and Information Gain. The process includes feature selection with Information Gain, calculating term weights with TF-IDF, and classification analysis with SVM. With a total of 365 bully data and 365 non-bully data. The test results show a significant increase in classification thanks to the use of Information Gain. The accuracy value of SVM without Information Gain is 0.822, while with Information Gain it is 0.830. This shows that selection of Information Gain features in SVM classification provides an increase in classification performance, which is caused by Information Gain's ability to select relevant features and the selection of C values to improve model adjustment. Keywords: bully, Information Gain, Classification, Support Vector Machine, Twitter

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407004010	T149440	T1494402024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1494402024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2024

Collation

xiv, VI-2 hlm.; ilus.; tab.; 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.754 07

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

media sosial
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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