BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features
Abstract
Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.
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References
C. S. Marzan and C. R. Ruiz, “Automated tobacco grading using image processing techniques and a convolutional neural network,” Int. J. Mach. Learn. Comput., vol. 9, no. 6, pp. 807–813, 2019.
Y. Sari and R. A. Pramunendar, “Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks,” Int. J. Comput. Trends Technol., vol. 50, no. 3, pp. 147–150, 2017.
S. K. Dasari, K. R. Chintada, and M. Patruni, “Flue-cured tobacco leaves classification: A generalized approach using deep convolutional neural networks,” SpringerBriefs Appl. Sci. Technol., no. 9789811066979, pp. 13–21, 2018.
K. Jha, A. Doshi, P. Patel, and M. Shah, “A comprehensive review on automation in agriculture using artificial intelligence,” Artif. Intell. Agric., vol. 2, pp. 1–12, 2019.
P. U. Rakhmawati, Y. M. Pranoto, and E. Setyati, “Klasifikasi Penyakit Daun Kentang Berdasarkan Fitur Tekstur Dan Fitur Warna Menggunakan Support Vector Machine,” Semin. Nas. Teknol. dan Rekayasa 2018, pp. 1–8, 2018.
S. Bakheet and A. Al-Hamadi, “Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification,” Comput. Biol. Med., vol. 137, no. June, p. 104781, 2021.
C. Zhang, Y. X. Tian, and Z. P. Fan, “Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN,” Int. J. Forecast., vol. 38, no. 3, pp. 1005–1024, 2022.
P. D. WANANDA, L. NOVAMIZANTI, and R. D. ATMAJA, “Sistem Deteksi Cacat Kayu dengan Metode Deteksi Tepi SUSAN dan Ekstraksi Ciri Statistik,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 6, no. 1, p. 140, 2018.
L. Cui, Y. Tao, J. Deng, X. Liu, D. Xu, and G. Tang, “BBO-BPNN and AMPSO-BPNN for multiple-criteria inventory classification,” Expert Syst. Appl., vol. 175, no. November 2020, p. 114842, 2021.
T. Adi Wicaksono and dan John Adler, “Analisis Metode GLCM dan SVM untuk Mendeteksi Cacat Kain,” J. Tek. Komput. Unikom-Komputika, vol. 7, no. 1, 2018.
T. Y. Prahudaya and A. Harjoko, “Metode Klasifikasi Mutu Jambu Biji Menggunakan Knn Berdasarkan Fitur Warna Dan Tekstur,” J. Teknosains, vol. 6, no. 2, p. 113, 2017.
C. Zhou, K. Yin, Y. Cao, and B. Ahmed, “Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China,” Eng. Geol., vol. 204, pp. 108–120, 2016.
Y. Agussationo, I. Soesanti, and W. Najib, “Klasifikasi Citra X-Ray Diagnosis Tuberkulosis Berbasis Fitur Statistis,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 3, pp. 736–745, 2018.
N. Kaur and V. Devendran, “Novel plant leaf disease detection based on optimize segmentation and law mask feature extraction with SVM classifier,” Mater. Today Proc., no. xxxx, 2020.
S. H. N. Pulung Nurtantio Andono, “Texture Feature Extraction in Grape Image Classification Using K-Nearest Neighbor,” Resti, vol. 6, no. 5, pp. 768–775, 2022.
A. Septiarini, Rizqi Saputra, Andi Tejawati, and Masna Wati, “Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, pp. 927–935, 2021.
A. Rao and S. B. Kulkarni, “A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods,” Int. J. Electr. Eng. Educ., pp. 1–19, 2020.
S. S. Panna and Betrisandi, “Klasifikasi Kelompok Usia Melalui Citra Wajah Berbasis Image Texture Analysis pada Sistem Automatic Video Filtering,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 429–434, 2019.
Y. Li, Z. Wang, and Y. Wei, “Pathways to progress sustainability: an accurate ecological footprint analysis and prediction for Shandong in China based on integration of STIRPAT model, PLS, and BPNN,” Environ. Sci. Pollut. Res., vol. 28, no. 39, pp. 54695–54718, 2021.
J. Trivedi, Y. Shamnani, and R. Gajjar, “Plant Leaf Disease Detection Using Machine Learning,” Commun. Comput. Inf. Sci., vol. 1214 CCIS, no. September, pp. 267–276, 2020.
A. Reema Matthew, A. Prasad, and P. Babu Anto, “A review on feature extraction techniques for tumor detection and classification from brain MRI,” 2017 Int. Conf. Intell. Comput. Instrum. Control Technol. ICICICT 2017, vol. 2018-Janua, pp. 1766–1771, 2018.
B. Jiang et al., “Evaluating traditional empirical models and BPNN models in monitoring the concentrations of chlorophyll-a and total suspended particulate of eutrophic and turbid waters,” Water (Switzerland), vol. 13, no. 5, 2021.
J. Zhao et al., “Prediction of temperature and CO concentration fields based on BPNN in low-temperature coal oxidation,” Thermochim. Acta, vol. 695, no. May 2020, p. 178820, 2021.
S.N.Sivanandam, Introduction to Genetic Algorithms. Springer Berlin Heidelberg, 2008.
S. Katoch, S. S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future, vol. 80, no. 5. Multimedia Tools and Applications, 2021.
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