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

PUTRI, AMELIA and Abdiansah, Abdiansah and Utami, Alvi Syahrini (2023) KLASIFIKASI KOMENTAR BULLY PADA TIKTOK MENGGUNAKAN SUPPORT VECTOR MACHINE DAN CHI-SQUARE FEATURE SELECTION. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Support Vector Machine, Chi-Square, TikTok
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Amelia Putri
Date Deposited:	31 Mar 2023 04:50
Last Modified:	31 Mar 2023 04:50
URI:	http://repository.unsri.ac.id/id/eprint/91602

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