A study on text classification for webmining based spatio temporal analysis of the spread of tropical diseases
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 to facilitate experts in taking proper actions. Meanwhile, 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 study aims to create a text classification system which classified the web document using several learning methods including naive Bayes, nearest neighbor, decision tree and support vector machine (SVM). The classification is intended to construct a spatio temporal analysis for documents classified into health. The result shows that naive Bayes and SVM achieve good performance (na├¤ve Bayes: 95% and SVM: 92%). Multinomial distribution of naive Bayes is able to normalize the length of document while SVM performs well in high-dimensional data.
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