Deteksi Defisiensi Unsur Hara Makro pada Tanaman Kopi berdasarkan Karakteristik Gejala Visual Daun menggunakan MTCD dan JST
Main Article Content
Abstract
Semua tanaman, termasuk kopi membutuhkan unsur hara yang cukup untuk penunjang pertumbuhan dan perkembangannya secara normal. Apabila kebutuhan hara tidak tercukupi dengan baik, tanaman akan kekurangan suplai makanan dan gejala khas muncul pada tanaman, seperti perubahan ukuran daun, klorosis, nekrosis dan lainnya yang akan terlihat jelas terutama pada organ daun. Gejala – gejala tersebut memberikan ciri khas atau pola pada daun berdasarkan defisiensi hara yang dialami suatu tanaman. Ciri khas tersebut kemudian diekstraksi menggunakan pengolahan citra digital (PCD) dengan menerapkan Multi Texton Cooccurrence Descriptor (MTCD). Metode MTCD akan melakukan penelusuran pada tiap bagian citra, kemudian mengekstrak piksel – piksel yang memiliki kesamaan nilai warna dan tepi. Fitur-fitur hasil ekstraksi digunakan untuk mewakili setiap citra dalam basis data, dan kemudian digunakan untuk klasifikasi dengan menerapkan jaringan saraf tiruan (JST). Hasil akurasi tertinggi yang dihasilkan klasifikasi adalah 0.706.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
P. dan M. Teguh Wahyudi, Ed., “Pengelolaan Hara Tanaman,” in Kopi: Sejarah, Botani, Proses Produksi, Pengolahan, Produksi Hilir, dan Sistem Kemitraan, Pertama., Gadjah Mada University Press, 2016, pp. 235–252.
K. N. Setiawan and I. M. S. Putra, “Klasifikasi Citra Mammogram Menggunakan Metode KMeans , GLCM , dan Support Vector Machine ( SVM ),” vol. 6, no. 1, pp. 13–24, 2018.
N. Ulinnuha and H. Sa’dyah, “Sistem temu kembali citra untuk e-commerce,” vol. 1, no. 1, pp. 35–41, 2015.
G. H. Liu and J. Y. Yang, “Image retrieval based on the texton co-occurrence matrix,” Pattern Recognit., vol. 41, no. 12, pp. 3521–3527, 2008.
G. Liu, L. Zhang, Y. Hou, Z. Li, and J. Yang, “Image retrieval based on multi-texton histogram,” Pattern Recognit., vol. 43, no. 7, pp. 2380–2389, 2010.
A. E. Minarno, Y. Munarko, A. Kurniawardhani, and F. Bimantoro,“Classification of Texture Using Multi Texton Histogram and Probabilistic Neural Network,” IOP Conf. Ser. Mater. Sci. Eng., vol. 105, no. 1, 2016.
M. Y. Qazi and M. S. Farid, “Content based image retrieval using localized multi-texton histogram,” Proc. - 11th Int. Conf. Front. Inf. Technol. FIT 2013, pp. 107–112, 2013.
X. Wang and Z. Wang, “A novel method for image retrieval based on structure elements’ descriptor,” J. Vis. Commun. Image Represent., vol. 24, no. 1, pp. 63–74, 2013.
S. Mohan and S. Suresh Kumar, “A New Shape Feature Extraction Method for Leaf Image Retrieval,” Lect. Notes Electr. Eng., vol. 221 LNEE, no. VOL. 1, 2013.
A. E. Minarno and N. Suciati, “Image Retrieval Using Multi Texton Co-,” vol. 67, no. 1, pp. 103–110, 2014.
A. Mozaffari, M. Emami, and A. Fathi, A comprehensive investigation into the performance, robustness, scalability and convergence of chaos-enhanced evolutionary algorithms with boundary constraints, vol. 52, no. 4. Springer Netherlands, 2019.
D. Wang, H. He, and D. Liu, “Intelligent Optimal Control with Critic Learning for a Nonlinear Overhead Crane System,” IEEE Trans. Ind. Informatics, vol. 14, no. 7, pp. 2932–2940, 2018.
D. Cho, Y. W. Tai, and I. S. Kweon, “Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes,” IEEE Trans. Image Process., vol. 28, no. 3, pp. 1054–1067, 2019.
A. Samsudin and R. Budiarto, “Lightweight and Cost-Effective,” pp. 525–526.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
R. Toufiq and M. R. Islam, “Face recognition system using PCA-ANN technique with feature fusion method,” 1st Int. Conf. Electr. Eng. Inf.Commun. Technol. ICEEICT 2014, 2014.
W. Yue, Z. Wang, H. Chen, A. Payne, and X. Liu, “Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis,” Designs, vol. 2, no. 2, p. 13, 2018.
N. G. A. . H. Saptarini and R. Y. Dillak, “Content Based Image Retrieval Menggunakan Moment Invariant , Tekstur Dan Backpropagation,” vol. 2012, no. semnasIF, pp. 86–91, 2012.
A. E. Minarno, A. S. Maulani, A. Kurniawardhani, and F. Bimantoro, “Comparison of Methods for Batik Classification Using Multi Texton Histogram,” vol. 16, no. 3, pp. 1358–1366, 2018.
S. E. Indraani, I. D. Jumaddina, S. Ridha, and S. Sinaga, “Implementasi Edge Detection Pada Citra Grayscale dengan Metode Operator Prewitt dan Operator Sobel,” pp. 1–5, 2014.
A. E. Minarno and N. Suciati, “Batik image retrieval based on color difference histogram and gray level co-occurrence matrix,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 12, no. 3, pp. 597–604, 2014.
R. Widodo, A. W. Widodo, and A. Supriyanto, “Pemanfaatan Ciri Gray Level Co-Occurrence Matrix ( GLCM ) Citra Buah Jeruk Keprok ( Citrus reticulata Blanco ) untuk Klasifikasi Mutu,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 5769–5776, 2018.
A. A. Kasim and A. Harjoko, “Klasifikasi Citra Batik Menggunakan Jaringan Syaraf Tiruan Berdasarkan Gray Level Co- Occurrence Matrices ( GLCM ),” Semin. Nas. Apl. Teknol. Inf. Yogyakarta, 21 Juni 2014, pp. 7–13, 2014.
O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. E. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, 2018.
W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016