CYBERBULLYING DETECTION ON TWITTER USES THE SUPPORT VECTOR MACHINE METHOD

Bayu Indra Kusuma; Aryo Nugroho

doi:10.52436/1.jutif.2024.5.1.809

Bayu Indra Kusuma Information Systems, Faculty of Computer Science, Universitas Narotama, Indonesia
Aryo Nugroho Information Systems, Faculty of Computer Science, Universitas Narotama, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.809

Keywords: c-svc, cyberbullying, data mining, Nu-SVC, Support Vector Machine

Abstract

Social media is a platform that provides facilities for users to engage in various social activities. However, the increasing popularity of social media in the modern era also cannot be separated from the occurrence of several negative impacts, one of which is cyberbullying. Cyberbullying is an action that is done online that can harm the mental and emotional condition of an individual. To reduce this problem, this research aims to investigate the performance of the C-SVC and Nu-SVC algorithms from the Support Vector Machine method in classifying cyberbullying sentences. The data used is comments data from the @puanmaharani_ri account on Twitter, which was collected from September 25, 2020, to September 29, 2022, totaling 5,000 data. After the data is collected, it is labeled and preprocessed, and then the data will be weighted using the TF-IDF method. The result of the TF-IDF will be displayed in the form of a word cloud. Next, the Support Vector Machine method will classify cyberbullying sentences using several percentages split combinations such as 60%, 70%, 80%, and 90%. The test results show that the C-SVC method has the highest accuracy of 79.6% at a 70% percentage split, while Nu-SVC has the highest accuracy of 78.9% at a 60% percentage split. From these results, it can be concluded that the Support Vector Machine method with the C-SVC algorithm provides better results than Nu-SVC in classifying cyberbullying sentences.

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