Performance improvement of Support Vector Machine (SVM) With information gain on categorization of Indonesian news documents

More news articles which are unstoppable increasing, causing problems with grouping news according to appropriate kind of label. Therefore it is necessary to deal with the problem of grouping news by it's category like business news, political news, and sports news. The categorization of news document belong to text classification domain, a Machine Learning topic as an approach that addressed this problem. Various algorithms have been used in previous studies such as Bayesian techniques, k-Nearest Neighborhood, Neural Networks, and Support Vector Machine (SVM). This study provides an understanding of the SVM method for news categorization on Indonesian news dataset that contain several types of news category. Problems in text classification is the number of features that affecting classification performance with SVM. Use of Information Gain as feature selection improve accuracy than without any feature selection. Our model give satisfying result with 98,057 % accuracy of Indonesia news classification. Improvement 2,9 points from 95,11% by SVM technique without feature selection.