Decision Support System for Bat Identification using Random Forest and C5.0
Abstract: Morphometric and
morphological bat identification are 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 problems for the beginners working with bat
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%. As many 50 rules were
implemented in the DSS to identify common and rare bat species with
morphometric and morphological attributes.
Author: Deden Sumirat Hidayat
Journal Code: jptkomputergg170108
