Identification of topics in News Articles Using Algorithm of Porter Stemmer Enhancement and Likelihood Classifier

Abstract

Every piece of information contained in a story sometimes has a variety of themes and seems not specific so there is difficulty in digesting information simultaneously. This requires grouping based on the topic relevance of the news. This grouping can make it easier for readers to get the information in accordance with the topic you want to read. Each news group must have different information characteristics so that we need a special algorithm that is able to handle topic discovery and classification using training data on many Indonesian news articles. This research will apply an algorithm of Porter Stemmer Enhancement in the stemming process and Likelihood method for news classification based on categories and identification of topics. Based on the test results using 900 training data and 90 test data, obtained a fairly high accuracy, namely 95.56% for category classification and 97.78% for topic identification.

Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor

“…Research [11] This research will apply the Porter Stemmer Enhancement algorithm in the stemming process and the Likelihood method for news classification by category and topic identification. A study [12] presents the implementation of multilabel classification using semantic features based on word2vec.…”