Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN


Purnawansyah Purnawansyah(1*); Aji Prasetya Wibawa(2); Triyanna Widyaningtyas(3); Haviluddin Haviluddin(4); Cholisah Erman Hasihi(5); Ming Foey Teng(6); Herdianti Darwis(7);

(1) Universitas Negeri Malang, Universitas Muslim Indonesia
(2) Universitas Negeri Malang
(3) Universitas Negeri Malang
(4) Universitas Mulawarman
(5) Universitas Muslim Indonesia
(6) American University of Sharjah
(7) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.

Keywords


Herbal Leaves Classification, GLCM-SVM, SVM Kernels, Convolutional Neural Network, GLCM-CNN

  
  

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doi  https://doi.org/10.33096/ilkom.v15i2.1759.382-389 		  

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