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Thesis Title
Text Classification Using Support Vector Machine for Webmining Based Spatio-Temporal Analysis of the Spread of Tropical Diseases.
Abstract
Tropical diseases such as Dengue Fever, Malaria and Bird Flu have become epidemic and particular problem in Indonesia. As the number of such cases increases, the availability of information regarding these diseases is important in order to help experts in taking proper actions. On the other hand, web mining is one of significant technologies applied to extract information from the web. By using web mining, spatio-temporal information of tropical diseases will be collected from the internet. This paper develops text classification system using Support Vector Machine to classify the downloaded text document from the Web for Web mining based spatio-temporal analysis system of the spread of tropical disease. Proper classification for every downloaded text document helps the information extraction system to construct spatio-temporal analysis so then can be visualized. While Support Vector Machine has shown its capabilities for classifying text since it works well in high-dimensional data and avoids the curse of dimensionality problem.
Main Paper
T. Joachims, 1998, “Text Categorization Using Support Vector Machines: Learning with Many Relevant Features”, Proc. of the European Conference on Machine Learning, Berlin, pp 137-142.
T. Joachims, 2001, “A Statistical Learning Model of Text Classification for Support Vector Machines”, Proc. of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, pp 128-136.
B. Prasetyo et al., 2008, “Desain Sistem Analisa Spatio-Temporal Penyebaran Penyakit Tropis Memakai Web Mining”, Proc. of National System and Information Conference, Bali, pp 44-49
