Machine Learning Pengenalan Anura Berdasarkan Corak Dan Warna

Authors

Hery Sunandar Universitas Budi Darma Medan

Keywords:

Machine Learning, Anura, Shades, Colors, Classification

Abstract

The Identification of Anura (frog) based on pattern and color is a complex problem that takes a long time and costs quite a lot. Therefore, this study uses Machine Learning techniques to develop a frog species recognition model based on pattern and color. Image data of different species, patterns, and colors were taken from reliable sources and then divided into training and test data sets. Deep learning techniques were used to study the visual patterns in frog images and develop classification models that can predict frog species based on their patterns and colors. The test results show that the developed model is a fairly high accuracy and can correctly identify frog species based on their patterns and colors. However, the identification of frogs based on pattern and color may not always be accurate in some cases of the images tested. therefore, Machine Learning techniques must be combined with other frog identification methods. The methods used are Contour, Hough Line Transform, K-Means, and Logistic Regression. This research can assist in the conservation of endangered frog species by being able to identify frog species quickly to monitor frog populations in the wild.

References

M. H. Rifqo and A. Wijaya, “IMPLEMENTASI ALGORITMA NAIVE BAYES DALAM PENENTUAN PEMBERIAN KREDIT,” 2017. [Online]. Available: www.ejournal.unib.ac.id/index.php/pseudocode

Y. Sitoto, T. Allo, V. Sofica, N. Hasan, and M. Septiani, “Penggunaan Metode Naïve Bayes Dalam Mengklasifikasi Pengangguran Pada Desa Bojong Kulur,” Bianglala Informatika , vol. 10, no. 1, p. 2022, 2022.

E. Retnoningsih and R. Pramudita, “Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python,” Bina Insani Ict Journal, vol. 7, no. 2, p. 156, 2020, doi: 10.51211/biict.v7i2.1422.

D. Sarkar, R. Bali, and T. Sharma, Practical Machine Learning with Rust. 2020. doi: 10.1007/978-1-4842-5121-8.

A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian Journal on Computer and Information Technology), vol. 5, no. 1, p[10] Y. Hasan and K. Siregar, “COMPUTER VISION IDENTIFICATION OF SPECIES, SEX, AND AGE OF INDONESIAN MARINE LOBSTERS,” JURNAL INFOKUM, vol. 9, no. 2, pp. 478–489, 2021, [Online]. Available: http://infor.seaninstitute.org/index.php/infokum/index

M. Kümmerer, M. Bethge, and T. S. A. Wallis, “DeepGaze III: Modeling free-viewing human scanpaths with deep learning,” J Vis, vol. 22, no. 5, pp. 1–27, Apr. 2022, doi: 10.1167/jov.22.5.7.

C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep Hough Voting for 3D Object Detection in Point Clouds,” 2019. doi: https://doi.org/10.48550/arXiv.1904.09664.

M. R. Larijani, E. A. Asli-Ardeh, E. Kozegar, and R. Loni, “Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means,” Food Sci Nutr, vol. 7, no. 12, pp. 3922–3930, Dec. 2019, doi: 10.1002/fsn3.1251.

J. M. Hilbe, “Practical Guide to Logistic Regression.”

S. Annas, A. Aswi, M. Abdy, and B. Poerwanto, “Stroke Classification Model using Logistic Regression,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Dec. 2021. doi: 10.1088/1742-6596/2123/1/012016.

W. H. Nugroho, S. Handoyo, Y. J. Akri, and A. D. Sulistyono, “Building Multiclass Classification Model of Logistic Regression and Decision Tree Using the Chi-Square Test for Variable Selection Method,” Journal of Hunan University Natural Sciences, vol. 49, no. 4, pp. 172–181, Apr. 2022, doi: 10.55463/issn.1674-2974.49.4.17.

p. 75–82, 2020, doi: 10.31294/ijcit.v5i1.7951.