Optimization of support vector machine with cubic kernel function to detect cyberbullying in social networks

Abstract

Social networking is a place where humans can interact using the internet network to be able to disseminate information, discuss, exchange ideas, pour out their hearts, and share activities. Many social networks are popularly used, one of which is Twitter. Information can be received quickly using Twitter. In addition, various government agencies also use Twitter to be able to interact directly with the community so that every government policy is disseminated through this social network. Every government policy neglects to reap the pros and cons of society, both collectively and individually. As a result of the pros and cons, a trial called cyberbullying was recorded. Cyberbullying in various studies has been carried out to change a person’s raw material so that with the application of information technology, identifying cyberbullying needs to be carried out further. The problem of cyberbullying is generally detected using the support vector machine (SVM) method. Cyberbullying detection is conducted in dealing with government policy data such as “cipta kerja” by using the SVM method which is optimized using the cubic kernel function. The accuracy value achieved in SVM uses a linear kernel function of 92.3% while using a cubic linear function of 90%.