Decision Support System for Bat Identification using Random Forest and C5.0

Deden Sumirat Hidayat, Imas Sukaesih Sitanggang, Gono Semiadi

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.


Keywords


bat identification; classification; C5.0; decision support system; random forest

Full Text:

PDF


DOI: http://doi.org/10.12928/telkomnika.v15i3.3638

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120
Fax: +62 274 564604

View TELKOMNIKA Stats