A. Febrian, Elly Matul I., I.Md. Agus S., M. Fajar, W. Jatmiko, D.H. Ramdhan, A. Bowolakso, P. Mursanto
Trichloroethylene (TRI) is chlorinated solvent which has been used in various materials for industrial and daily task, such as dry-cleaners, ink composer, and medicine ingredients. It's has been known that TRI can penetrate human liver, and prolong exposure can evoke permanent damage to liver or cancer. In this research, we tried to create an automation tool that can help us analyzed and predict TRI level in human liver. The prediction will be based on liver images which analyzed using FCM, BPNNs, FLVQ, or FLVQ-PSO. In this research, the images of white mice liver that have been exposed to TRI are used. Our experiments show that the best accuracy achieved by BPNNs and 45 features from images which have been processed with KPCA. This combination accuracy is 99.12%. © 2011 IEEE.
Faculty of Computer Science, Universitas Indonesia, Depok, West Java, Indonesia; Mathematics Department, Universitas Negeri Surabaya, Surabaya, East Java, Indonesia; Computer Science Department, Universitas Udayana, Denpasar, Bali, Indonesia; Faculty of Public Health, Universitas Indonesia, Depok, West Java, Indonesia; Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, West Java, Indonesia
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