Segmentasi Citra Kupu-Kupu Menggunakan Metode Multilevel Thresholding

Ainin Maftukhah(1*), Abdul Fadlil(2), S Sunardi(3),

(1) Universitas Ahmad Dahlan, Yogyakarta, Indonesia
(2) Universitas Ahmad Dahlan, Yogyakarta, Indonesia
(3) Universitas Ahmad Dahlan, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


Land conversion, pollution, logging, and the use of pesticides are the main causes of butterfly extinction. This used 50 types of butterflies using different RGB colors obtained from the Kaggle website. The goal is to separate the butterfly object from the background and produce the best accuracy from the segmentation proses. The method used is Multilevel Thresholding. The results of preprocessing on the image using Multilevel Thresholding segmentation are able to identify colors and butterfly objects. The first step is RGB image input, then the image is Segmented using Multilevel Thresholding. After that, the output is displaying the image, and using a threshold value of 0-255 with the results of image segmentation, the threshold value separates the object and the background. Multilevel Thresholding segmentation with color and shape identification obtains threshold values of 100 from the dataset train, 100, and 110 from the test dataset and 140, and 150 from the validation dataset. It was concluded that the results of threshold value of the Multilevel Thresholding segmentation obtained good results

Full Text:

PDF

References


A. Rajab and D. Asriady, Keanekaragaman Jenis Kupu-Kupu Papilionidae. Tn-Babul, 2015.

A. Damara Gonggoli et al., “Identifikasi Jenis Kupu-Kupu (Lepidoptera) di Universitas Palangka Raya,” Bioeksperimen, vol. 7, no. 1, p. 16, 2021.

A. Pulung Nurtantio, T.Sutojo, and Muljono, Pengolahan Citra Digital. Andi Publisher, 2017.

A. T. H. Al-Rahlawee and J. Rahebi, “Multilevel Thresholding of Images With Improved Otsu Thresholding by Black Widow Optimization Algorithm,” Multimed. Tools Appl., vol. 80, no. 18, pp. 28217–28243, Jul. 2021, doi: 10.1007/s11042-021-10860-w.

A. K. M. Khairuzzaman and S. Chaudhury, “Masi Entropy Based Multilevel Thresholding for Image Segmentation,” Multimed. Tools Appl., vol. 78, no. 23, pp. 33573–33591, Dec. 2019, doi: 10.1007/s11042-019-08117-8.

S. R. Kaki et al., “Sistemasi: Jurnal Sistem Informasi Perbandingan Algoritma Multi-Thresholding, Konversi Biner, Low-Pass Filtering pada,” 2021. [Online]. Available: http://sistemasi.ftik.unisi.ac.id

B. Lei and J. Fan, “Multilevel Minimum Cross Entropy Thresholding: A Comparative Study,” Appl. Soft Comput. J., vol. 96, Nov. 2020, doi: 10.1016/j.asoc.2020.106588.

R. Andrian, S. Anwar, M. A. Muhammad, and A. Junaidi, “Identifikasi Kupu-Kupu Menggunakan Ekstraksi Fitur Deteksi Tepi (Edge Detection) dan Klasifikasi K-Nearest Neighbor (KNN),” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 2, Sep. 2019, doi: 10.28932/jutisi.v5i2.1744.

A. Prahara and E. Iman Heri Ujianto, “Multilevel Thresholding Segmentasi Citra Warna Menggunakan Logarithmic Decreasing Inertia Weight Particle Swarm Optimization Multilevel Thresholding Color Image Segmentation Using Logarithm Decreasing Inertia Weight Particle Swarm Optimization,” SAINTEKS, vol. 19, no. 1, 2022.

R. Srikanth and K. Bikshalu, “Multilevel Thresholding Image Segmentation Based on Energy Curve with Harmony Search Algorithm,” Ain Shams Eng. J., vol. 12, no. 1, pp. 1–20, Mar. 2021, doi: 10.1016/j.asej.2020.09.003.

A. Saleh, N. P. Dharshinni, and F. Azmi, “Face Recognition using Self Organizing Map Based on Multi-Level Thresholding and Features Extraction,” in Journal of Physics: Conference Series, Jun. 2021, vol. 1898, no. 1. doi: 10.1088/1742-6596/1898/1/012045.

A. Hermawan, T. Rahmad Effendi, and N. Fadillah, “Terbit online pada laman web jurnal: https://ejurnalunsam.id/index.php/jicom/ Deteksi Kematangan Buah Pisang Berdasarakan Kulit Menggunakan Metode Multi-Level Thresholding dan YCbCr,” 2021, [Online]. Available: https://ejurnalunsam.id/index.php/jicom/

S. Zhao et al., “Multilevel Threshold Image Segmentation with Diffusion Association Slime Mould Algorithm and Renyi’s Entropy for Chronic Obstructive Pulmonary Disease,” Comput. Biol. Med., vol. 134, no. May, p. 104427, 2021, doi: 10.1016/j.compbiomed.2021.104427.

Y. He, G. Zhang, and X. Zhang, “Multilevel Thresholding Based on Fuzzy Masi Entropy,” in Proceedings of 2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021, Jan. 2021, pp. 403–407. doi: 10.1109/ICPECA51329.2021.9362561.

Z. Yan, J. Zhang, and J. Tang, “Sine Cosine Algorithm for Underwater Multilevel Thresholding Image Segmentation,” in 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast, Oct. 2020. doi: 10.1109/IEEECONF38699.2020.9389009.

S. R. Ratna, “Pengolahan Citra Digital dan Histogram Dengan Phyton dan Text Editor Phycharm,” 2020.

D. Razabni, E. Medinah, and S. Sinurat, “Analisa dan Perbandingan Algoritma Otsu Thresholding dengan Algoritma Region Growing Pada Segmentasi Citra Digital,” J. Comput. Syst. Informatics, vol. 2, no. 1, 2020.

Rudiono and D. Avianto, “Implementasi Ekstraksi Ciri Histogram dan K-Nearest Neighbor untuk Klasifikasi Jenis Tanah di Kota Banjar,” J. Buana Inform., vol. 10, 2019.

A. A. Fitrawan, M. N. Shodiq, D. H. Kusuma, and F. R. U. Jannah, “Sistem Pendeteksi Jarak pada Objek Realtime Video Berdasarkan Luas Kontur Menggunakan Metode Circle Hough,” Semin. Nas. Terap. Ris. Inov., vol. 6, 2020.

S. Joseph and O. O. Olugbara, “Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation,” Diagnostics, vol. 12, no. 2, Feb. 2022, doi: 10.3390/diagnostics12020344.

W. Li, A. N. Joseph Raj, T. Tjahjadi, and Z. Zhuang, “Digital hair removal by deep learning for skin lesion segmentation,” Pattern Recognit., vol. 117, Sep. 2021, doi: 10.1016/j.patcog.2021.107994.

T. Acharya and A. K. Ray, Image Processing Principles and Applications. Wiley-Interscience, 2005.

B. Küçükuğurlu and E. Gedikli, “Symbiotic Organisms Search Algorithm for multilevel thresholding of images,” Expert Syst. Appl., vol. 147, Jun. 2020, doi: 10.1016/j.eswa.2020.113210.

C. Solomon and T. Breckon, “Fundamentals of Digital Image Processing A Practical Approach with Examples in Matlab,” 2018.

E. R. Davies, Computer and Machine Vision: Theory, Algorithms, Practicalities. Elsevier, 2012.




DOI: http://dx.doi.org/10.30645/j-sakti.v7i2.665

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats