Skripsi

KLASIFIKASI KOMENTAR BULLY PADA TIKTOK MENGGUNAKAN SUPPORT VECTOR MACHINE DAN CHI-SQUARE FEATURE SELECTION

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Penilaian

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TikTok has been named the world’s most popular social media platform because it has been downloaded by more than 1 billion users. The high level of TikTok use makes it allows for free opinion to anyone. It can cause bullying on TikTok because some users do not understand the ethics of socializing through social media. The victims experienced higher levels of depression than other verbal acts of violence. TikTok developers can prevent bullying by using policies such as word detection and filtering features that indicate comments fall under the category of bullying or non-bullying comments. To classify bullying forms in TikTok, this research uses the Support Vector Machine method to classify the data and Chi-Square Feature Selection to reduce the number of features in the comment data. Tests were carried out using the Linear, Polynomial, and RBF kernel functions with the C parameter, namely 0,1, 1, and 10 for each kernel. The results of this research show that the Support Vector Machine method with Chi-Square Feature Selection has a better performance. This was proven by the increased accuracy in RBF kernel C=0,1 which was 0,20.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
	T99844	T916022023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T916022023

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xx, 86 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Pemrosesan data
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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