Klasifikasi Hewan Mamalia Berdasarkan Bentuk Wajah Menggunakan Fitur Histogram of Oriented dan Metode Support Vector Machine
DOI:
https://doi.org/10.32736/sisfokom.v11i1.1205Keywords:
HOG, K-Fold Cross Validation, Mammals, SVMAbstract
Mammals have several characteristics that can be distinguished, such as footprints, voice, and face shape. Mammals can be recognized. To classify the face shape of mammals, the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) methods can be used. This study uses the LHI-Animal-Faces dataset which is taken as many as 15 species of mammals, where each type of mammal is selected 60 images and resized to 150x150 pixels. The image is converted into a grayscale image for the HOG feature extraction process. Furthermore, the classification process uses SVM. The kernels used are Linear, Polynomial, and Gaussian kernels. The testing process uses K-Fold Cross Validation. The folds used are 3-fold, 4-fold, 5-fold, 6-fold, and 10-fold. The performance of the HOG feature and the SVM method that gives the best results is the Linear kernel using 10-fold with an accuracy value of 96.55%, precision of 77.92%, and recall of 74.11%. The sequence of kernels that give the best results in this test is the Linear kernel, Polynomial kernel, and Gaussian kernel.References
S. Solari and R. J. Baker, “Mammal Species of the World: A Taxonomic and Geographic Reference by D. E. Wilson; D. M. Reeder,” J. Mammal., vol. 88, no. 3, pp. 824–830, 2007, doi: 10.1644/06-MAMM-R-422.1.
M. N. Alli and S. Viriri, “Animal identification based on footprint recognition,” IEEE Int. Conf. Adapt. Sci. Technol. ICAST, 2013, doi: 10.1109/ICASTech.2013.6707488.
M. E. Al Rivan and Y. Yohannes, “Klasifikasi Mamalia Berdasarkan Bentuk Wajah Dengan k-NN Menggunakan Fitur CAS dan HOG,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 5, no. 2, pp. 169–176, 2019, doi: 10.35957/jatisi.v5i2.139.
Y. Yohannes and M. E. Al Rivan, “Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia,” Petir, vol. 13, no. 1, pp. 80–85, 2020, doi: 10.33322/petir.v13i1.908.
Y. Yohannes, Y. P. Sari, and I. Feristyani, “Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 1, pp. 84–97, 2019, doi: 10.28932/jutisi.v5i1.1584.
Z. Cao, J. C. Principe, B. Ouyang, F. Dalgleish, and A. Vuorenkoski, “Marine Animal Classification Using Combined CNN and Hand-designed Image Features,” Ocean. 2015 - MTS/IEEE Washingt., pp. 2–7, 2015, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963985281&partnerID=40&md5=4898b0d41c0eec6db77a6de0fe5c9a31.
N. Manohar, Y. H. S. Kumar, and G. H. Kumar, “Supervised and unsupervised learning in animal classification,” 2016 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2016, pp. 156–161, 2016, doi: 10.1109/ICACCI.2016.7732040.
Y. H. S. Kumar, N. Manohar, and H. K. Chethan, “Animal classification system: A block based approach,” Procedia Comput. Sci., vol. 45, no. C, pp. 336–343, 2015, doi: 10.1016/j.procs.2015.03.156.
S. Taheri and Ö. Toygar, “Animal classification using facial images with score-level fusion,” IET Comput. Vis., vol. 12, no. 5, pp. 679–685, 2018, doi: 10.1049/iet-cvi.2017.0079.
F. Alharbi, A. Alharbi, and E. Kamioka, “Animal species classification using machine learning techniques,” MATEC Web Conf., vol. 277, p. 02033, 2019, doi: 10.1051/matecconf/201927702033.
F. Fandiansyah, J. Y. Sari, and I. P. Ningrum, “Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis Dan K Nearest Neighbor,” J. Inform., vol. 11, no. 2, 2017, doi: 10.26555/jifo.v11i2.a5998.
M. B. Pranoto, K. N. Ramadhani, and A. Arifianto, “Face Detection System Menggunakan Metode Histogram of Oriented Gradients ( HOG ) dan Support Vector Machine ( SVM ) Face Dtection System using Histogram of Oriented Gradients ( HOG ) Method amd Support Vector Machine ( SVM ),” e-Proceeding Eng., vol. 4, no. 3, pp. 5038–5045, 2017.
D. Amputri, S. Nadra, G. Gasim, and M. E. Al Rivan, “Perbandingan jarak potret dan resolusi kamera pada tingkat akurasi pengenalan angka kwh meter menggunakan svm,” J. Inform. Glob., vol. 8, no. 1, pp. 7–12, 2017, doi: http://dx.doi.org/10.36982/jig.v8i1.218.
L. Farsiah, T. Fuadi Abidin, and K. Munadi, “Klasifikasi Gambar berwarna menggunkan K-Nearest Neighbor dan Support Vector Machine,” no. Snastikom, pp. 1–5, 2010.
R. Brehar and S. Nedevschi, “Local information statistics of LBP and HOG for pedestrian detection,” in 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP), 2013, pp. 117–122, doi: 10.1109/ICCP.2013.6646093.
B. Santosa and A. Umam, “Data mining dan big data analytics, ed. 2,” 2018.
Z. Si and S. Zhu, “Learning Hybrid Image Templates (HIT) by Information Projection,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1354–1367, 2012.
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