Article Sidebar

Submitted
June 26, 2023
Published
September 30, 2023
Download
PDF
Statistic
Read Counter : 341 Download : 14

Main Article Content

Abstract

This research aims to develop an image classification method for the panthera genus using a deep learning approach based on Convolutional Neural network (CNN). The panthera genus includes large species such as tigers, lions, leopards, and jaguars, which share similarities in appearance but also differences in fur patterns, body size, and habitat. Image classification of the panthera genus is important in various applications, including wildlife conservation and biological research. In this study, image datasets of tigers, lions, and leopards were collected from various sources to a total of 6,290 images. The proposed method involves image pre-processing, such as resizing, converting and normalization, and the use of a Convolutional Neural network (CNN) model to perform classification. The CNN model is implemented and trained using training data to recognize specific visual patterns in the images of each species. The results of this study show that the CNN-based deep learning approach can achieve high accuracy in the classification of panthera genus images of 85.21%. This method can correctly distinguish between tiger, lion, and leopard images based on unique visual features. In addition, the deep learning approach also offers advantages in efficiency and scalability to cope with the large number of images in the dataset. This research makes an important contribution to the development of wildlife image classification methods using a CNN-based deep learning approach.

Keywords

Deep Learning Genus Panthera Convolution Neural Network

Article Details

References

  1. Annas T.S, T. (2019). Perbandingan Model Warna RGB , HSL dan HSV Sebagai Fitur dalam Prediksi Cuaca pada Citra Langit menggunakan. Teknik Informatika, 9.
  2. Anwar, G. A., & Riminarsih, D. (2019). Klasifikasi Citra Genus Panthera Menggunakan Metode Convolutional Neural Network (Cnn). Jurnal Ilmiah Informatika Komputer, 24(3), 220–228. https://doi.org/10.35760/ik.2019.v24i3.2364
  3. Arisudana, K., Kurniawan, W., Putro, F. W., Studi, P., Perangkat, R., Teknologi, F., & Industri, I. (2020). Sistem pendeteksi kerusakan luar angkutan umum. 175–187.
  4. Bismi, W., & Harafani, H. (2022). Perbandingan Metode Deep Learning dalam Mengklasifikasi Citra Scan MRI Penyakit Otak Parkinson. InComTech : Jurnal Telekomunikasi Dan Komputer, 12(3), 177. https://doi.org/10.22441/incomtech.v12i3.15068
  5. Calik, N., Belen, M. A., & Mahouti, P. (2020). Deep learning base modified MLP model for precise scattering parameter prediction of capacitive feed antenna. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 33(2). https://doi.org/10.1002/jnm.2682
  6. Hurtik, P., Molek, V., & Hula, J. (2020). Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function. IEEE Transactions on Fuzzy Systems, 28(7), 1195–1204. https://doi.org/10.1109/TFUZZ.2019.2911494
  7. Kurniastuti, I., Yuliati, E. N. I., Yudianto, F., & Wulan, T. D. (2022). Determination of Hue Saturation Value (HSV) color feature in kidney histology image. Journal of Physics: Conference Series, 2157(1), 012020. https://doi.org/10.1088/1742-6596/2157/1/012020
  8. Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture (Switzerland), 11(8), 1–18. https://doi.org/10.3390/agriculture11080707
  9. Meijer, D., Scholten, L., Clemens, F., & Knobbe, A. (2019). A defect classification methodology for sewer image sets with convolutional neural networks. Automation in Construction, 104(December 2018), 281–298. https://doi.org/10.1016/j.autcon.2019.04.013
  10. Oken, L. (2008). Okens Lehrbuch der Naturgeschichte: 3. Theil. Zoologie ; 2. Abt. Fleischthiere. https://books.google.co.id/books?id=S5o5AAAAcAAJ
  11. Piosenka, G. (2022). 10 Big Cats of the Wild - Image Classification. KAGGLE. https://www.kaggle.com/datasets/gpiosenka/cats-in-the-wild-image-classification
  12. Prasetyo, E. (2012). Data mining : konsep dan aplikasi menggunakan MATLAB (1st ed.). CV. Andi Offset. https://elibrary.bsi.ac.id/readbook/200350/data-mining-konsep-dan-aplikasi-menggunakan-matlab
  13. Pratiwi, N. K. C., Fu’adah, Y. N., & Edwar, E. (2021). Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture. Jurnal Infotel, 13(2), 54–62. https://doi.org/10.20895/infotel.v13i2.642
  14. Pujiati, R., & Rochmawati, N. (2022). Identifikasi Citra Daun Tanaman Herbal Menggunakan Metode Convolutional Neural Network ( CNN ). JINACS (Journal of Informatics and Computer Science), 03, 351–357.
  15. R.I., Pocock, & F.R.S. (1916). XXXVI.—On the tooth-change, cranial characters, and classification of the snow-leopard or ounce (Felis uncia). Annals and Magazine of Natural History Zoology, Botany, and Geology, 18(105), 306–316. https://doi.org/10.1080/00222931608693854
  16. Ramadhani, M. (2018). Klasifikasi Jenis Jerawat Berdasarkan Tekstur dengan Menggunakan Metode GLCM. E-Proceding of Enggineering, 5(1), 870–876.
  17. Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Ma, J., & Wang, K. (2021). Image Preprocessing in Classification and Identification of Diabetic Eye Diseases. Data Science and Engineering, 6(4), 455–471. https://doi.org/10.1007/s41019-021-00167-z
  18. Sekar, V., Jiang, Q., Shu, C., & Khoo, B. C. (2019). Fast flow field prediction over airfoils using deep learning approach. Physics of Fluids, 31(5). https://doi.org/10.1063/1.5094943
  19. Sultana, J., Usha Rani, M., & Farquad, M. A. H. (2019). Student’s performance prediction using deep learning and data mining methods. International Journal of Recent Technology and Engineering, 8(1 Special Issue 4), 1018–1021.
  20. Suryanto, A., Andrianto, B., Alvianda, B., Saputro, H. A., & Siregar, M. S. (2014). PENGKLASIFIKASI GENUS PANTHERA (HARIMAU, SINGA, JAGUAR DAN MACAN TUTUL) DENGAN METODE NAIVE BAYES. Universitas Brawijaya, 5.