Issue

Vol. 5, No. 2, May 2020

Soybean Disease Detection with Feature Selection Using Stepwise Regression Algorithm: LVQ vs LVQ2

https://doi.org/10.22219/kinetik.v5i2.919
Nida Muhamad
Universitas Diponegoro
Sukmawati Nur Endah
Universitas Diponegoro
Eko Adi Sarwoko
Universitas Diponegoro
Priyo Sidik Sasongko
Universitas Diponegoro

Corresponding Author(s) : Sukmawati Nur Endah

sukmawati020578@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 2, May 2020

Abstract

ndonesia's soybean needs increase from year to year. But according to data from the Badan Pusat Statistik (BPS) the amount of national soybean productivity is still low, so the fulfillment of soybean needs is done by importing soybeans from several countries such as China, Ukraine, Canada, Malaysia, and the United States. Low soybean productivity is caused by several factors. One of the causes is disease. This study aims to create a soybean disease detection by applying Learning Vector Quantization 2 (LVQ2) neural network algorithm(ANN) and Stepwise Regression Algorithm attribute selection. The attribute variables used consisted of 35 symptoms of the disease in soybean crop data. The data used in this study is a soybean dataset taken from University of California Irvine Machine Learning Repository as much as 200 data. The distribution of training data and test data is done by the k-fold cross validation method with a value of k = 10. The result of the study shows that the best paramater use in lVQ2. The results showed that the best parameters in LVQ2 is learning rate (α) value of 0.3; epsilon 0.04; and maximum epoch 100. While the best attribute selection uses the parameter p to enter and p to remove of  0.15 which produces 17 selected attributes such as date, plant stand, precipitation, leaves, leaf spot halo, leaf spot margins, leafspot size, leaf mildew, stem canker, stem fungi, external decay, fruit pods, fruit spots, seeds, mold growth, seed discolor, roots. The best results in this study resulted in an accuracy of 90.5%, 9.5% error rate, 90.5% sensitivity, and 98.94% specificity

Keywords

Soybean Plant Disease Detection Learning Vector Quantization LVQ2 Stepwise Regression Algorithm
Muhamad, N., Endah, S. N., Sarwoko, E. A., & Sasongko, P. S. (2020). Soybean Disease Detection with Feature Selection Using Stepwise Regression Algorithm: LVQ vs LVQ2. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(2), 119-128. https://doi.org/10.22219/kinetik.v5i2.919
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References
  1. Mapegau, “Pengaruh Cekaman Air Terhadap Pertumbuhan Dan Hasil Tanaman Kedelai (Glycine Max L. Merr),” No. 2001, Pp. 43–51, 2006.
  2. F. Y. Wicaksono, A. W. Irwan, and R. Fitriani, “Response of soybean (Glycine max) var. Wilis due to application of N, P, K and guano fertilizer dosages on Inceptisols Jatinangor,” J. Kultiv., Vol. 16, No. 2, Pp. 333–339, 2017. https://doi.org/10.24198/kultivasi.v16i2.13223
  3. BPS, “Produksi Kedelai Menurut Provinsi Tahun 2014-2018,” 2018.
  4. S. Sulandari, S. Hartono, Y. M. S. Maryudani, and Y. B. Paradisa, “Detection and Distribution of Soybean Mosaic Virus (SMV) and Soybean Stunt Virus (SSV) at Soybean Production Centers in Indonesia,” J. Perlindungan Tanam. Indones., Vol. 18, No. 2, Pp. 71–78, 2014.
  5. H. Semangun, “Penyakit-Penyakit Tanaman Pangan Di Indonesia,” Gadjah Mada Univ. Press, 2004.
  6. P. Dongre and T. Verma, “A Survey of Identification of Soybean Crop Diseases,” Int. J. Adv. Res. Comput. Eng. Technol., Vol. 1, No. 8, Pp. 361–364, 2012.
  7. A. Susanti, M. Faizah, M. Lutfi, and S. Khamid, “Penekanan Penyakit Karat Daun Pada Kedelai Akibat Phakopsora pachyrhizi Syd . Menggunakan Mikoriza Indigenous Pada Tanah Litosol,” Vol. 2, No. 1, Pp. 23–31, 2018.
  8. D. Apriyanto, O. Hendra, and A. Mulyadi, “Penampilan Penggerek Polong Kedelai , Etiella zinckenella Treitschke ( Lepidoptera : Pyralidae ), dan Pemilihan Inang pada Kedelai dan Kacang Tanah Performance of Soybean Pod Borer , Etiella zinckenella Treitschke ( Lepidoptera : Pyralidae ), and Host Preference on Soybean and Groundnut,” Vol. 12, No. 1, Pp. 62–67, 2009.
  9. W. Batchelor, “Development of a Neural Network for Soybean Rust Epidemics,” Trans. ASAE, Vol. 40, No. 1, Pp. 247–252, 1997. https://doi.org/10.13031/2013.21237
  10. K. S. Kim, T. C. Wang, and X. B. Yang, “Simulation of Apparent Infection Rate to Predict Severity of Soybean Rust Using a Fuzzy Logic System,” Phytopathology, Pp. 1122–1131, 2005. https://doi.org/10.1094/PHYTO-95-1122
  11. I. Rahmon, Adebola_Akinsanya, and M. . Eze, “A Neuro-Fuzzy System For Diagnosis of Soya-Beans Diseases,” Res. J. Math. Comput. Sci., 2018. https://doi.org/10.28933/rjcms-2018-04-0501
  12. Nopiyanti, E. Tita, and A. Maesya, “Sistem Identifikasi Penyakit Tanaman Kacang Kedelai Menggunakan Metode Naive Bayes.,” 2015.
  13. Joshi, Sneha, and M. Borse, “Detection and Prediction of Diabetes Mellitus Using Back-Propagation Neural Network,” in International Conference on Micro-Electronics and Telecommunication Engineering, ICMETE 2016, Pp. 110–113, 2017. https://doi.org/10.1109/ICMETE.2016.11
  14. Budianita, Elvia, and M. Firdaus, “Diagnosis Penyakit Kejiwaan Menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization2 (LVQ 2) (Studi Kasus : Rumah Sakit Jiwa Tampan Pekanbaru),” Vol. 13, No. 2, Pp. 146–50, 2016.
  15. Rahadian, B. Aristyo, C. Dewi, and B. Rahayudi, “The Performance of Genetic Algorithm Learning Vector Quantization 2 Neural Network on Identification of the Types of Attention Deficit Hyperactivity Disorder,” in 2017 International Conference on Sustainable Information Engineering and Technology (SIET), Pp. 337–41, 2017. https://doi.org/10.1109/SIET.2017.8304160
  16. Maulana, Fadil, and S. N. Endah, “Comparison Selection of Attributes in Preprocessing Data for Diagnosis of Diabetes,” in 1st International Conference on Informatics and Computational Sciences, ICICoS 2017, Pp. 141–46, 2018. https://doi.org/10.1109/ICICOS.2017.8276352
  17. Michalski, Ryszard S, and R. L Chilausky, “Learning by Being Told and Learning from Examples: An Experimental Comparison of the Two Methods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis,” Int. J. Policy Anal. Inf. Syst., Vol. 4, No. 2, Pp. 125–161, 1980.
  18. Hanke, John E., and D. W. Wichern, Business Forecasting Ninth Edition. New Jersey: Pearson, 2008.
  19. Han, Jiawei, M. Kamber, and J. Pei, Data Mining Concept and Techniques. Waltham: Morgan Kaufmann, 2011.
  20. Refaeilzadeh, Payam, L. Tang, and H. Lu, “Cross-Validation.” 2008.
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