This study aims to clarify tweets on twitter using the Support Vector Machine and Information Gain methods. The clarification itself aims to find a hyperplane that separates the negative and positive classes. In the research stage, there is a system process, namely text mining, text processing which has stages of tokenizing, filtering, stemming, and term weighting. After that, a feature selection is made by information gain which calculates the entropy value of each word. After that, clarify based on the features that have been selected and the output is in the form of identifying whether the tweet is bully or not. The results of this study found that the Support Vector Machine and Information Gain methods have sufficiently maximum results.
Improving the Efficiency of Term Weighting in Set of Dynamic Documents
