Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Triando Hamonangan Saragih; Diny Melsye Nurul Fajri; Alfita Rakhmandasari

doi:10.22219/kinetik.v5i1.1012

Issue

Vol. 5, No. 1, February 2020

Issue Published : Feb 6, 2020

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

https://doi.org/10.22219/kinetik.v5i1.1012

Triando Hamonangan Saragih

Universitas Lambung Mangkurat

http://orcid.org/0000-0003-4346-3323

Diny Melsye Nurul Fajri

Universitas Brawijaya

Alfita Rakhmandasari

Universitas Brawijaya

Corresponding Author(s) : Triando Hamonangan Saragih

triando.saragih@ulm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 1, February 2020
Article Published : Feb 6, 2020

Abstract

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

Keywords

Decision Tree Identification Jatropha Curcas K-Nearest Neighbor Modified K-Nearest Neighbor

Saragih, T. H., Fajri, D. M. N., & Rakhmandasari, A. (2020). Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1), 55-60. https://doi.org/10.22219/kinetik.v5i1.1012

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References

J. Rodrigues et al., “Storage Stability of Jatropha Curcas L. Oil Naturally Rich in Gamma-Tocopherol,” Ind. Crops Prod., vol. 64, pp. 188–193, 2015. https://doi.org/10.1016/j.indcrop.2014.10.048
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T. H. Saragih, D. M. N. Fajri, A. Hamdianah, and W.F Mahmudy, “Jatropha curcas disease identification using Fuzzy Neural Network,” 2017 International Conference on Sustainable Information Engineering and Technology (SIET), pp 305-309, 2017. https://doi.org/10.1109/SIET.2017.8304153
D. M. N. Fajri, T. H. Saragih, A. Hamdianah, and W.F Mahmudy, “Optimized fuzzy neural network for Jatropha Curcas plant disease identification,” 2017 International Conference on Sustainable Information Engineering and Technology (SIET), pp 297-304, 2017. https://doi.org/10.1109/SIET.2017.8304152
T. H. Saragih, D. M. N. Fajri, Y. P. Anggodo, A. Latief and W.F Mahmudy, “Jatropha Curcas Disease Identification With Extreme Learning Machine,” Indonesian Journal of Electrical Engineering and Computer Science, vol 12, no.2, 2018. http://doi.org/10.11591/ijeecs.v12.i2.pp883-888
A. V Sitanggang, A. A. Soebroto, and T. Yulianti, “Sistem Pakar Identifikasi Penyakit Tanaman Jarak Pagar (Jatropha Curcas L.) dengan menggunakan Metode Naive Bayes (Expert System of Jatropha Curcas L. Identification by using Naive Bayes Method),” DORO Repos. J. Mhs. PTIIK Univ. Brawijaya, vol. 7, no. 4, 2016.
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M. Vadovsky and J. Paralic, “Parkinson’s Disease Patients Classification Based on The Speech Signals,” in IEEE 15th International Symposium on Applied Machine Intelligence and Informatics, 2017. https://doi.org/10.1109/SAMI.2017.7880326
W. M. Baihaqi, N. A. Setiawan, and I. Ardiyanto, “Rule Extraction for Fuzzy Expert System to Diagnose Coronary Artery Disease,” in 1st International Conference on Information Technology, Information Systems and Electrical Engineering, 2016. https://doi.org/10.1109/ICITISEE.2016.7803062
Q. Kotimah, W. F. Mahmudy, and V. N. Wijayaningrum, “Optimization of Fuzzy Tsukamoto Membership Function using Genetic Algorithm to Determine the River Water Quality,” Int. J. Electr. Comput. Eng., vol. 7, no. 5, pp. 2838–2846, 2017. http://doi.org/10.11591/ijece.v7i5.pp2838-2846
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D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, New Jersey: A John Wiley & Sons, Inc., 2005.
J. Han, M. Kamber, and J. Pei, Data Mining: Concept and Techniques. Waltham, USA: Morgan Kaufmann, 2012.

References

J. Rodrigues et al., “Storage Stability of Jatropha Curcas L. Oil Naturally Rich in Gamma-Tocopherol,” Ind. Crops Prod., vol. 64, pp. 188–193, 2015. https://doi.org/10.1016/j.indcrop.2014.10.048

C. M. Fernandez, L. Fiori, M. J. Ramos, A. Perez, and J. F. Rodriguez, “Supercritical Extraction and Fractionation of Jatropha Curcas L. Oil for Biodiesel Production,” J. Supercrit. Fluids, vol. 97, pp. 100–106, 2015. https://doi.org/10.1016/j.supflu.2014.11.010

T. H. Saragih, A. A. Soebroto, and T. Yulianti, “Sistem Pakar Diagnosa Penyakit Tanaman Jarak Pagar (Jantropha Curcas L.) menggunakan Metode Dempster-Shafer (Expert System Diagnosis of Jatropha Curcas L. using the Dempster-Shafer Method),” DORO Repos. J. Mhs. PTIIK Univ. Brawijaya, vol. 7, no. 4, 2016.

T. H. Saragih, D. M. N. Fajri, A. Hamdianah, and W.F Mahmudy, “Jatropha curcas disease identification using Fuzzy Neural Network,” 2017 International Conference on Sustainable Information Engineering and Technology (SIET), pp 305-309, 2017. https://doi.org/10.1109/SIET.2017.8304153

D. M. N. Fajri, T. H. Saragih, A. Hamdianah, and W.F Mahmudy, “Optimized fuzzy neural network for Jatropha Curcas plant disease identification,” 2017 International Conference on Sustainable Information Engineering and Technology (SIET), pp 297-304, 2017. https://doi.org/10.1109/SIET.2017.8304152

T. H. Saragih, D. M. N. Fajri, Y. P. Anggodo, A. Latief and W.F Mahmudy, “Jatropha Curcas Disease Identification With Extreme Learning Machine,” Indonesian Journal of Electrical Engineering and Computer Science, vol 12, no.2, 2018. http://doi.org/10.11591/ijeecs.v12.i2.pp883-888

A. V Sitanggang, A. A. Soebroto, and T. Yulianti, “Sistem Pakar Identifikasi Penyakit Tanaman Jarak Pagar (Jatropha Curcas L.) dengan menggunakan Metode Naive Bayes (Expert System of Jatropha Curcas L. Identification by using Naive Bayes Method),” DORO Repos. J. Mhs. PTIIK Univ. Brawijaya, vol. 7, no. 4, 2016.

E. K. Hashi, M. Shahiduzzaman, and M. R. Hasan, “An Expert Clinical Decision Support System to Predict Disease using Classification Techniques,” in International Conference on Electrical, Computer and Communication Engineering, 2017. https://doi.org/10.1109/ECACE.2017.7912937

M. Vadovsky and J. Paralic, “Parkinson’s Disease Patients Classification Based on The Speech Signals,” in IEEE 15th International Symposium on Applied Machine Intelligence and Informatics, 2017. https://doi.org/10.1109/SAMI.2017.7880326

W. M. Baihaqi, N. A. Setiawan, and I. Ardiyanto, “Rule Extraction for Fuzzy Expert System to Diagnose Coronary Artery Disease,” in 1st International Conference on Information Technology, Information Systems and Electrical Engineering, 2016. https://doi.org/10.1109/ICITISEE.2016.7803062

Q. Kotimah, W. F. Mahmudy, and V. N. Wijayaningrum, “Optimization of Fuzzy Tsukamoto Membership Function using Genetic Algorithm to Determine the River Water Quality,” Int. J. Electr. Comput. Eng., vol. 7, no. 5, pp. 2838–2846, 2017. http://doi.org/10.11591/ijece.v7i5.pp2838-2846

B. Subba, S. Biswas, and S. Karmakar, “A Neural Network based System for Intrusion Detection and Attack Classification,” in 22nd National Conference on Communication, 2016. https://doi.org/10.1109/NCC.2016.7561088

F. Al Huda, W. F. Mahmudy, and H. Tolle, “Android Malware Detection Using Backpropagation Neural Network,” Indones. J. Electr. Eng. Comput. Sci., vol. 4, no. 1, pp. 240–244, 2016. http://doi.org/10.11591/ijeecs.v4.i1.pp240-244

S. Tan, “An Effective Refinement Strategy for KNN Text Classifier,” Expert Syst. Appl., vol. 30, no. 2, pp. 290–298, 2006. https://doi.org/10.1016/j.eswa.2005.07.019

C.-L. Liu, C.-H. Lee, and P.-M. Lin, “A Fall Detection System using k-Nearest Neighbor,” Expert Syst. Appl., vol. 37, no. 10, pp. 7174–7181, 2010. https://doi.org/10.1016/j.eswa.2010.04.014

H. Parvin, H. Alizadeh, and B. Minaei-Bidgoli, “MKNN: Modified K-Nearest Neighbor,” in Proceedings of the World Congress on Engineering and Computer Science, 2008.

T. H. Simanjuntak, W. F. Mahmudy, and Sutrisno, “Implementasi Modified K-Nearest Neighbor dengan Otomatisasi Nilai K pada Pengklasifikasian Penyakit Tanaman Kedelai (Implementation of Modified K-Nearest Neighbor with Automation of K Value on Classification of Soya Plant Disease),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 2, pp. 75–79, 2017.

D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, New Jersey: A John Wiley & Sons, Inc., 2005.

J. Han, M. Kamber, and J. Pei, Data Mining: Concept and Techniques. Waltham, USA: Morgan Kaufmann, 2012.

Issue

Vol. 5, No. 1, February 2020

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Corresponding Author(s) : Triando Hamonangan Saragih

Abstract

Keywords

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