With the technology development, a large amount of information such as news articles are available over the internet. Hence, text categorization, such as applying classification as one of data mining task, is needed. The major issue in text categorization is the high dimensionality of data. Therefore, we need to select some representative attributes to improve the performance of text categorization. One of techniques to complete this task is feature selection. Feature selection can reduce high dimensionality. Thus, the classifier effectiveness can improve. Among many method, is a filterbased feature selection. This research examined and compared some feature selection techniques toward Indonesian news articles by applying filter model. These models are discussed: Gini Index for text categorization, CHI, Information Gain, Expected Cross Entropy, Weight Of Evidence and Orthogonal Centroid Feature Selection (OCFS).