DOI: https://doi.org/10.14710/jtsiskom.2021.13807

Optimasi SVM menggunakan algoritme grid search untuk identifikasi citra biji kopi robusta berdasarkan circularity dan eccentricity

SVM optimization using a grid search algorithm to identify robusta coffee bean images based on circularity and eccentricity

Department of Informatics, Universitas Singaperbangsa Karawang. Jl. HS.Ronggo Waluyo, Puseurjaya, Kec. Telukjambe Timur, Kabupaten Karawang, Jawa Barat 41361, Indonesia

Received: 26 Jun 2020; Revised: 10 Aug 2021; Accepted: 4 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to cite (IEEE): H. Apriani, J. H. Jaman, and R. I. Adam, "Optimasi SVM menggunakan algoritme grid search untuk identifikasi citra biji kopi robusta berdasarkan circularity dan eccentricity," Jurnal Teknologi dan Sistem Komputer, vol. 10, no. 1, pp. 12-19, Jan. 2022. https://doi.org/10.14710/jtsiskom.2021.13807
Citation Format:
Abstract
Coffee variety is one of the main factors affecting the quality and price of coffee, so it is important to recognize coffee varieties. This study aims to optimize the recognition of robusta coffee beans based on circularity and eccentricity image features using a support vector machine (SVM) and Grid search algorithm. The methods used included image acquisition, preprocessing, feature extraction, classification, and evaluation. Circularity and eccentricity are used in the feature extraction process, while the grid search algorithm is used to optimize SVM parameters in the classification process for four different kernels. This study produced the best classification model with the highest accuracy of 94% for the RBF and Polynomial kernels.
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Keywords: offee bean identification; grid search; parameter optimization; support vector machine
Funding: Universitas Singaperbangsa Karawang

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  1. C. Zhang, F. Liu, and Y. He, “Identification of coffee bean varieties using hyperspectral imaging: Influence of preprocessing methods and pixel-wise spectra analysis,” Scientific Reports, vol. 8, no. 1, pp. 1–11, 2018. doi: 10.1038/s41598-018-20270-y
  2. Y. Hendrawan, S. Widyaningtyas, and S. Sucipto, “Computer vision for purity, phenol, and pH detection of Luwak coffee green bean,” Telkomnika: Telecommunication Computing Electronics and Control, vol. 17, no. 6, pp. 3073–3085, 2019. doi: 10.12928/telkomnika.v17i6.12689
  3. Kementerian Pertanian, Outlook 2018 Komoditas Pertanian Subsektor Perkebunan Kopi. Jakarta: Kementrian Pertanian, 2019
  4. J. A. Vignoli, M. C. Viegas, D. G. Bassoli, and M. T. Benassi, “Roasting process affects differently the bioactive compounds and the antioxidant activity of arabica and robusta coffees,” Food Research International, vol. 61, pp. 279–285, 2014. doi: 10.1016/j.foodres.2013.06.006
  5. E. R. Arboleda, A. C. Fajardo, and R. P. Medina, “Classification of coffee bean species using image processing, artificial neural network and k nearest neighbors, ” in IEEE International Conference on Innovative Research and Development, Bangkok, Thailand, Jun. 2018. doi: 10.1109/ICIRD.2018.8376326
  6. M. M. Sebatubun and M. A. Nugroho, “Ekstraksi fitur circularity untuk pengenalan varietas kopi arabika,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 4, pp. 283-289, 2017. doi: 10.25126/jtiik.201744505
  7. J. P. Rodríguez, D. C. Corrales, J. N. Aubertot, and J. C. Corrales, “A computer vision system for automatic cherry beans detection on coffee trees,” Pattern Recognition Letters, vol. 136, pp. 142–153, 2020. doi: 10.1016/j.patrec.2020.05.034
  8. M. Widyaningsih, “Identifikasi kematangan buah apel dengan gray level co-occurrence matrix (GLCM),” Jurnal SAINTEKOM, vol. 6, no. 1, pp. 71-88, 2017. doi: 10.33020/saintekom.v6i1.7
  9. A. Lawi and Y. Adhitya, “Classifying physical morphology of cocoa beans digital images using multiclass ensemble least-squares support vector machine,” Journal of Physics: Conference Series, vol. 979, 012029, 2018. doi: 10.1088/1742-6596/979/1/012029
  10. I. Campaña, A. Benito-Calvo, A. Pérez-González, J. M. Bermúdez de Castro, and E. Carbonell, “Assessing automated image analysis of sand grain shape to identify sedimentary facies, Gran Dolina archaeological site (Burgos, Spain),” Sedimentary Geology, vol. 346, pp. 72–83, 2016. doi: 10.1016/j.sedgeo.2016.09.010
  11. W. Chen, H. R. Pourghasemi, and S. A. Naghibi, “A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China,” Bulletin of Engineering Geology and the Environment, vol. 77, no. 2, pp. 647–664, 2018. doi: 10.1007/s10064-017-1010-y
  12. M. Wang, Y. Wan, Z. Ye, and X. Lai, “Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm,” Information Sciences, vol. 402, pp. 50–68, 2017. doi: 10.1016/j.ins.2017.03.027
  13. N. B. Bahadure, A. K. Ray, and H. P. Thethi, “Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM,” International Journal of Biomedical Imaging, vol. 2017, 9749108, 2017. doi: 10.1155/2017/9749108
  14. L. S. Wei, Q. Gan, and T. Ji, “Skin disease recognition method based on image color and texture features,” Computational and Mathematical Methods in Medicine, vol. 2018, 8145713, 2018. doi: 10.1155/2018/8145713
  15. K. Petrujkić et al., “Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis,” European Journal of Radiology, vol. 119, 108634, 2019. doi: 10.1016/j.ejrad.2019.08.003
  16. E. Etriyanti, D. Syamsuar, and N. Kunang, “Implementasi data mining menggunakan algoritme naive bayes classifier dan c4.5 untuk memprediksi kelulusan mahasiswa,” Telematika, vol. 13, no. 1, pp. 56–67, 2020. doi: 10.35671/telematika.v13i1.881
  17. R. Munawarah, O. Soesanto, and M. R. Faisal, “Penerapan metode support vector machine,” KLIK - Kumpulan Jurnal Ilmiah Ilmu Komputer, vol. 4, no. 1, pp. 103–113, 2016
  18. M. A. Ebrahimi, M. H. Khoshtaghaza, S. Minaei, and B. Jamshidi, “Vision-based pest detection based on SVM classification method,” Computers and Electronics in Agriculture, vol. 137, pp. 52–58, 2017. doi: 10.1016/j.compag.2017.03.016
  19. I. Syarif, A. Prugel-Bennett, and G. Wills, “SVM parameter optimization using grid search and genetic algorithm to improve classification performance,” Telkomnika: Telecommunication Computing Electronics and Control, vol. 14, no. 4, pp. 1502–1509, 2016. doi: 10.12928/telkomnika.v14i4.3956
  20. M. K. V. Joshi and D. D. Shah, “Hybrid of the fuzzy c means and the thresholding method to segment the image in identification of cotton bug,” International Journal of Applied Engineering Research , vol. 13, no. 10, pp. 7466–7471, 2018
  21. R. A. Asmara and T. A. Heryanto, “Klasifikasi varietas biji kopi arabika menggunakan ekstraksi bentuk dan tekstur,” in Seminar Informatika Aplikatif Polinema, Malang, Indonesia, Jun. 2019, pp. 316–322
  22. N. Musyaffa and B. Rifai, “Model support vector machine berbasis particle swarm optimization untuk prediksi penyakit liver,” JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), vol. 3, no. 2, pp. 189–194, 2018
  23. P. Thanh Noi and M. Kappas, “Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery,” Sensors, vol. 18, no. 1, 2017. doi: 10.3390/s18010018
  24. H. Wang, B. Zheng, S. W. Yoon, and H. S. Ko, “A support vector machine-based ensemble algorithm for breast cancer diagnosis,” European Journal of Operational Research, vol. 267, no. 2, pp. 687–699, 2018. doi: 10.1016/j.ejor.2017.12.001

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