Classification Analysis of MotoGP Comments on Media Social Twitter Using Algorithm Support Vector Machine and Naive Bayes

The high comment about the event of a motor racing motoGP race in a print media and electronic media, making the event makes the conversation of many people in the real world and in cyberspace. Especially in the digital era today is very easy for people to get the information they want, either through the website or through existing media social and sometimes the info is loaded in real time at the same time comment on the show about trending topics that exist in cyberspace. The curiosity of the public about info-info or comments circulating about the motoGP racing makes the conversation in the existing media social so that the topic becomes a popular topic in media social that post about the race of the motoGP race. This paper will do research how accurate the comments about the existing motoGP in existing media social such as twitter which became a forum for society to talk about the race of the motoGP race. In this paper will apply two classification algorithms to test how accurate the information or comments that become a lot of people talk through media social twitter. This paper will apply the Support Vector Machine and Navie Bayes algorithms in text mining processing. The result of SVM algorithm accuracy value is 95.50% while the value of NB accuracy is 93.00%.