Decision Support System for Bats Identification using Random Forest and C5.0
Morphometric and morphological bat identification is a conventional method of identification and requires precision, significant experience, and encyclopedic knowledge. Morphological features of a species may sometimes similar to that of another species and this causes several problem for the beginners working with bat’s taxonomy. The purpose of the study was to implement and conduct the random forest and C5.0 algorithm analysis in order to decide characteristics and carry out identification of bat species. It also aims at developing supporting decision-making system based on the model to find out the characteristics and identification of the bat species. The study showed that C5.0 algorithm prevailed and was selected with the mean score of accuracy of 98.98, while the mean score of accuracy for the random forest was 97.26. Total rules to be implemented in the DSS to identify species with morphometric and morphological attributes and rare species were 50 rules.
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