Taufik Fuadi Abidin, Rahmad Dimyathi, Ridha Ferdhiana
With the rapid maturity of internet and web technology over the last decades, the number of Indonesian online news articles is growing rapidly on the web at a pace we never experienced before. In this paper, we introduce a combination of rule-based and machine learning approach to find the sentences that have tropical disease information in them, such as the incidence date and the number of casualty, and we measure its accuracy. Given a set of web pages in tropical disease topic, we first extract the sentences in the pages that match contextual and morphological patterns for a date and number of casualty using a rule-based algorithm. After that, we classify the sentences using Support Vector Machine and collect the sentences that have tropical disease information in them. The results show that the proposed method works well and has good accuracy. © 2014 IEEE.
Department of Informatics, College of Science, Syiah Kuala University, Aceh, 23111, Indonesia; Department of Mathematics, College of Science, Syiah Kuala University, Indonesia; Department of Statistics, College of Science, Syiah Kuala University, Aceh, 23111, Indonesia
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