Implementasi Pengolahan Citra Untuk Identifikasi Daun Tanaman Obat Menggunakan Levenberg-Marquardt Backpropagation
The Implementation of Digital Image Processing to Identify Medicinal Leaves Using Levenberg-Marquardt Backpropagation
Abstract
Digital Image processing implementation can be applied to identify medicinal leaves, because it can help the elderly and people with color-blindness in identifying medicinal leave to be consumed and in avoiding reading errors, since some leaves have similar shape and color . In this discussion, the feature-extractions are using color and shape features, and using Levenberg-Marquardt for pattern recognition algorithm. The success of this medicinal plant identification system resulted in fairly good accuracy. The backpropagation network architecture used two hidden layers with 10 and 5 neurons. Data training is using 60 training leaf images with 15 images each of 5 types: green betel leaf, red betel, soursop, castor and aloe vera. Then, offline testing is using 20 test images for each of 4 images from 5 types with the accuracy of 85%. Meanwhile the online (realtime) test is using 20 times for each leaf types so the accuracy is 88%.
Downloads
References
Amrullah, I. N., & Sutojo, T. (2014). Identifikasi daun berdasarkan faktor kekompakan dan faktor kebundaran bentuk daun 1,2. 13(4), 198–205.
Ayuningsih, K., Sari, Y. A., & Adikara, P. P. (2019). Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naïve Bayes Classifier. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 3(4), 3166–3173.
Fausett, L. (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications Fundamentals of Neural Networks: Architectures, Algorithms and Applications. In Journal of chromatography. B, Analytical technologies in the biomedical and life sciences (1st ed., Vol. 814, Issue 2). Prentice Hall, c1994 , 1994.
Gavin, H. P. (2019). The Levenburg-Marqurdt Algorithm For Nonlinear Least Squares Curve-Fitting Problems. Duke University, 1–19. http://people.duke.edu/~hpgavin/ce281/lm.pdf
Iriyanto, & Zaini. (2014). Pengolahan Citra Digital (Maret 2014, Issue April). Anugrah Utama Raharja (AURA).
Jamaliah, I., W, R. N., & Maimunah. (2017). Identifikasi jenis daun tanaman obat hipertensi berdasarkan citra rgb menggunakan jaringan syaraf tiruan. 5(1), 1–11.
Shofrotun, F., Sutojo, T., Ignatius, D. R., & Setiadi, M. (2018). Identifikasi Tumbuhan Obat Herbal Berdasarkan Citra Daun Menggunakan Algoritma Gray Level Co-occurence Matrix dan K-Nearest Neighbor. 6(November 2017), 51–56. https://doi.org/10.14710/jtsiskom.6.2.2018.51-56
Srimulyani, W., & Musdholifah, A. (2019). Identification of Rice Variety Using Geometric Features and Neural Network. 13(3), 301–312.
Sutiko, Indriyati, Priyo, S. S., Helmie, A. W., Indra, W., Nurdin, B., & K, T. W. (2016). Backpropagation dan Aplikasinya. Ilmu Komputer: Studi Kasus Dan Aplikasinya, 134–146.
Tandrian, A. H., Kusnadi, A., Teknik, F., Nusantara, U. M., & Serpong, G. (2018). Pengenalan Pola Tulang Daun Dengan Jaringan Syaraf Tiruan Backpropagation. X(2), 53–58.
Wijaya, N., & Ridwan, A. (2019). Klasifikasi Jenis Buah Apel Dengan Metode K-Nearest Neighbours. Sisfokom, 08(1), 74–78.
Yu, H., & Wilamowski, B. M. (2010). Levenberg-Marquardt Training. In Physics of the Solid State (Vol. 46, Issue 8, pp. 12-1-12–16). Auburn Univercity. https://doi.org/10.1134/1.1788770
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
