Malika Soybean Quality Classification using GLCM and Lacunarity features
Klasifikasi Kualitas Kedelai Malika menggunakan fitur GLCM dan Lacunarity
Abstract
Kualitas kedelai digunakan sebagai acuan kompussi kandungan yang terdapat pada kedelai itu. Kedelai yang berkualitas adalah kedelai yang tidak cacat dan ukuranya tidak terlalu kecil. Pada penelitian ini menggunakan jenis ekstraksi fitur tekstur dikarenakan sangat cocok dengan karakter dari kedelai malika. Penelitian ini menggunakan metode Gray Level Co-occurrence Matrices (GLCM) dan Lacunarity untuk ekstraksi fitur tekstur. Untuk melakukan klasifikasi digunakan metode Multi-Layer Perceptron (MLP) dan Naïve Bayes Classifiers (NBC). akurasi terbaik yang dihasilkan dari proses klasifikasi kualitas kedelai Malika yaitu 0,98 dengan klasifikasi MLP dan gabungan ekstraksi tektur menggunakan GLCM dan Lacunarity.
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[6] R. A. Saputra, Suharyanto, S. Wasiyanti, D. F. Saefudin, A. Supriyatna, and A. Wibowo, “Rice Leaf Disease Image Classifications Using KNN Based on GLCM Feature Extraction,†Journal of Physics: Conference Series, vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012080.
[7] Ş. Öztürk and B. Akdemir, “ScienceDirect ScienceDirect ScienceDirect Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM , Application of Feature Extraction and Classification Methods for and GLCM , Histopathological Image using a SFT,†Procedia Computer Science, vol. 132, no. Iccids, pp. 40–46, 2018, doi: 10.1016/j.procs.2018.05.057.
[8] M. R. T. Dale, “Lacunarity analysis of spatial pattern: A comparison,†Landscape Ecology, vol. 15, no. 5, pp. 467–478, 2000, doi: 10.1023/A:1008176601940.
[9] Y. Quan, Y. Xu, Y. Sun, and Y. Luo, “Lacunarity analysis on image patterns for texture classification,†Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 160–167, 2014, doi: 10.1109/CVPR.2014.28.
[10] A. Maulana, D. E. Yuliana, and D. A. W. Kusumastutie, “Weld Defect Classifier Using GLCM Extraction and ANN,†JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer, vol. 2, no. 2, 2022, doi: 10.32503/jtecs.v2i2.2701.
[11] P. N. Rahayu, D.- Erwanto, and A. R. Putri, “Extraction of Timber’s Features using GLCM, Color Moment and Isotropic Undecimated Wavelet Transform (IUWT),†JAREE (Journal on Advanced Research in Electrical Engineering), vol. 6, no. 1, pp. 1–6, 2022, doi: 10.12962/jaree.v6i1.147.
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Published
2024-01-30
How to Cite
RAHAYU, Putri Nur; RIZAL, Royb Fatkhur; YUMONO, Fajar.
Malika Soybean Quality Classification using GLCM and Lacunarity features.
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer, [S.l.], v. 4, n. 1, p. 83-90, jan. 2024.
ISSN 2776-6195.
Available at: <https://ejournal.uniska-kediri.ac.id/index.php/JTECS/article/view/4942>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.32503/jtecs.v4i1.4942.
Section
Komputer
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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