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

Deden Sumirat Hidayat, Imas Sukaesih Sitanggang, Gono Semiadi


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.


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

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P. Fedor, I. Malenovsky, J. Vanhara, W. Sierka, and J. Havel, “Thrips (Thysanoptera) identification using artificial neural networks,” Bull. Entomol. Res., vol. 98, no. 05, pp. 437–447, 2008.

T. N. Ananthakrishnan, “Perspectives and dimensions of phenotypic plasticity in insects,” Insect Phenotypic Plast. Divers. Responses. Sci. Publ. Enfield, NH, pp. 1–23, 2005.

T. A. Ransaleleh, R. R. A. Maheswari, P. Sugita, and W. Manalu, “Identifikasi Kelelawar Pemakan Buah Asal Sulawesi Berdasarkan Morfometri (The Morphometric Identification of Celebes Fruit Bats,” J. Vet., vol. 14, no. 4, 2013.

J. Vaňhara, N. Muráriková, I. Malenovsk`y, and J. Havel, “Artificial neural networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae),” Biologia (Bratisl)., vol. 62, no. 4, pp. 462–469, 2007.

Y. A. Kartika, “Pengenalan Jenis Katak dan Kodok Berdasarkan Ciri Bentuk dan Penklasifikasi dengan Pendekatan Statistik, Fuzzy dan Jaringan Syaraf Tiruan,” University of Indonesia, 2011.

A. H. Serna and L. F. J. Sequra, “Automatic identification of species with neural networks,” PeerJ, vol. 2, p. e563, 2014.

T. Lucas, “Bat Identification with Gaussian Process Learning,” 2011.

A. Prawesti, “Sistem Pakar Identifikasi Varietas Ikan Mas (Cyprinus carpio) Berdasarkan Karakteristik Morfologi dan Tingkah Laku,” 2013.

N. Larios, B. Soran, L. G. Shapiro, G. Martínez-Muñoz, J. Lin, and T. G. Dietterich, “Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification.,” in ICPR, 2010, vol. 1, no. 2, p. 7.

M. Immitzer, C. Atzberger, and T. Koukal, “Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data,” Remote Sens., vol. 4, no. 9, pp. 2661–2693, 2012.

K. De Bruyne, B. Slabbinck, W. Waegeman, P. Vauterin, B. De Baets, and P. Vandamme, “Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning,” Syst. Appl. Microbiol., vol. 34, no. 1, pp. 20–29, 2011.

H. R. Huber, J. C. Jorgensen, V. L. Butler, G. Baker, and R. Stevens, “Can salmonids (Oncorhynchus spp.) be identified to species using vertebral morphometrics?,” J. Archaeol. Sci., vol. 38, no. 1, pp. 136–146, 2011.

C. Guisande, A. Manjarrés-Hernández, P. Pelayo-Villamil, C. Granado-Lorencio, I. Riveiro, A. Acuña, E. Prieto-Piraquive, E. Janeiro, J. M. Matías, C. Patti, and others, “IPez: an expert system for the taxonomic identification of fishes based on machine learning techniques,” Fish. Res., vol. 102, no. 3, pp. 240–247, 2010.

M. E. Barkworth, D. R. Cutler, J. S. Rollo, S. W. L. Jacobs, and A. Rashid, “Morphological identification of genomic genera in the Triticeae,” Breed. Sci., vol. 59, no. 5, pp. 561–570, 2009.

D. R. Cutler, T. C. Edwards Jr, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler, “Random forests for classification in ecology,” Ecology, vol. 88, no. 11, pp. 2783–2792, 2007.

D. W. Armitage and H. K. Ober, “A comparison of supervised learning techniques in the classification of bat echolocation calls,” Ecol. Inform., vol. 5, no. 6, pp. 465–473, 2010.

K. Zhang and B. Hu, “Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles,” Remote Sens., vol. 4, no. 6, pp. 1741–1757, 2012.

I. S. Sitanggang, R. Yaakob, N. Mustapha, and A. N. Ainuddin, “A decision tree based on spatial relationships for predicting hotspots in peatlands,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 12, no. 2, pp. 511–518, 2014.

P. Thariqa, I. S. Sitanggang, and L. Syaufina, “Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 14, no. 2, pp. 684–691, 2016.



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