@article{(Open Science Index):https://publications.waset.org/pdf/10009,
	  title     = {Indonesian News Classification using Support Vector Machine},
	  author    = {Dewi Y. Liliana and  Agung Hardianto and  M. Ridok},
	  country	= {},
	  institution	= {},
	  abstract     = {Digital news with a variety topics is abundant on the
internet. The problem is to classify news based on its appropriate
category to facilitate user to find relevant news rapidly. Classifier
engine is used to split any news automatically into the respective
category. This research employs Support Vector Machine (SVM) to
classify Indonesian news. SVM is a robust method to classify
binary classes. The core processing of SVM is in the formation of an
optimum separating plane to separate the different classes. For
multiclass problem, a mechanism called one against one is used to
combine the binary classification result. Documents were taken
from the Indonesian digital news site, www.kompas.com. The
experiment showed a promising result with the accuracy rate of 85%.
This system is feasible to be implemented on Indonesian news
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {5},
	  number    = {9},
	  year      = {2011},
	  pages     = {1015 - 1018},
	  ee        = {https://publications.waset.org/pdf/10009},
	  url   	= {https://publications.waset.org/vol/57},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 57, 2011},