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ASME Press Select Proceedings

International Conference on Computer and Electrical Engineering 4th (ICCEE 2011)

By
Jianhong Zhou
Jianhong Zhou
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ISBN:
9780791859841
No. of Pages:
698
Publisher:
ASME Press
Publication date:
2011

In the era of internet technology, many cases of tropical diseases like malaria, leprosy, and dengue fever are reported online. These online facts can be very useful to track the spread of the diseases. Studies in classifying tropical disease web pages from a large set of Indonesian web pages have not yet been recognized. In this paper, we built classifiers using Support Vector Machine (SVM), Na1ve Bayesian, and K-Nearest Neighbors. We generated dictionaries of n-gram terms for both positive (tropical disease) and negative (non-tropical disease) classes and used the dictionaries to extract feature attributes of the pages. The experimental results show that SVM with polynomial kernel is the best classifier model when compared to the other models and methods. The F-measure and accuracy of the model are 95.52% and 99.59% respectively.

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