Classification of Emotions in Indonesian Texts Using K-nn Method

This paper aims to classify texts in Indonesian language into emotion expression classes. The data were taken from 6 basic emotion classes whose training documents and test documents were obtained from articles in www.kompas.com, www.suaramerdeka.com, and www.detik.com. The text weighing was processed by using TFID method which is an integration of Term Frequency (TF) and Inverse Document Frequency (IDF). In the classification process, K-Nearest Neighbor (K-NN) was used to see how far this method could classify emotion expression of Indonesian language. The test shows that the classification of the Indonesian texts for the six basic emotion classes by using K-NN method results in accurateness percentage of 71.26%, obtained at k=40 as the optimum value.