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

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 367 times
PDF view: 141 times
     

Digital Object Identifier

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

Cite

References


D. S. Utomo, E. B. E. Kristiani, and A. Mahardika, “Pengaruh Lokasi Tumbuh Terhadap Kadar Flavonoid, Fenolik, Klorofil, Karotenoid Dan Aktivitas Antioksidan Pada Tumbuhan Pecut Kuda (Stachytarpheta Jamaicensis),” Bioma, vol. 22, no. 2, pp. 143–149, 2020.

Meiriyama and Sudiadi, “Penerapan Algoritma Random Forest Untuk Klasifikasi Jenis Daun Herbal,” Jtsi, vol. 3, no. 1, pp. 131–138, 2022.

A. Herdiansah, R. I. Borman, D. Nurnaningsih, A. A. J. Sinlae, and R. R. Al Hakim, “Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 2, pp. 388–395, 2022, doi: 10.30865/jurikom.v9i1.3846.

Haryono, Khairul Anam, and Azmi Saleh, “Autentikasi Daun Herbal Menggunakan Convolutional Neural Network dan Raspberry Pi,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 9, no. 3, pp. 278–286, 2020, doi: 10.22146/.v9i3.302.

M. S. Mustafa, Z. Husin, W. K. Tan, M. F. Mavi, and R. S. M. Farook, “Development of Automated Hybrid Intelligent System for Herbs Plant Classification and Early Herbs Plant Disease Detection,” Neural Comput Appl, vol. 32, no. 15, pp. 11419–11441, 2020, doi: 10.1007/s00521-019-04634-7.

Danilo Gomes de Arruda, “Perkembangan Teknologi Informasi Komunikasi / ICT dalam Berbagai Bidang,” Jurnal Fakultas Teknik UNISA Kuningan, vol. 2, no. 2, p. 6, 2021.

M. Dastbaz, “Green Information Technology,” 2015.

I. P. Arisanti and Y. Yamasari, “Mengenali Jenis Tanaman Obat Berbasis Pola Citra Daun dengan Algoritma K-Nearest Neighbors,” Journal of Informatics and Computer Science (JINACS), vol. 3, no. 02, pp. 95–103, 2021, doi: 10.26740/jinacs.v3n02.p95-103.

F. Zikra, K. Usman, and R. Patmasari, “Deteksi Penyakit Cabai Berdasarkan Citra Daun Menggunakan Metode Gray Level Co-Occurence Matrix Dan Support Vector Machine,” Seminar Nasional Hasil Penelitian dan Pengabdian Masyarakat, vol. ISSN: 2598, no. E-ISSN: 2598-0238, p. 105, 2021.

R. Suhendra, I. Juliwardi, and S. Sanusi, “Identifikasi dan Klasifikasi Penyakit Daun Jagung Menggunakan Support Vector Machine,” Jurnal Teknologi Informasi, vol. 1, no. 1, pp. 29–35, 2022, doi: 10.35308/.v1i1.5520.

A. J. Rozaqi, A. Sunyoto, and M. rudyanto Arief, “Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network,” Creative Information Technology Journal, vol. 8, no. 1, p. 22, 2021, doi: 10.24076/citec.2021v8i1.263.

Leong K and Tze Lim, “Plant Leaf Diseases Identification using Convolutional Neural Network with Treatment Handling System,” in IEEE, Shah Alam: IEEE, Jun. 2020. doi: 10.1109/I2CACIS49202.2020.9140103.

Y. Zhang, J. Cui, Z. Wang, J. Kang, and Y. Min, “Leaf Image Recognition Based on Bag of Features,” Applied Sciences (Switzerland), vol. 10, no. 15, 2020, doi: 10.3390/app10155177.

A. Riadi and R. Sulaehani, “Analisis Implementasi Preprocessing Dengan Otsu-Gaussian Pada Pengenalan Wajah,” ILKOM Jurnal Ilmiah, vol. 11, no. 3, pp. 200–205, 2019, doi: 10.33096/ilkom.v11i3.457.200-205.

T. Ahmed, T. Rahman, B. B. Roy, and J. Uddin, “Drone Detection by Neural Network Using GLCM and SURF Features,” Journal of Information Systems and Telecommunication, vol. 9, no. 33, pp. 15–23, 2021, doi: 10.52547/jist.9.33.15.

V. K. Chauhan, K. Dahiya, and A. Sharma, “Problem Formulations and Solvers in Linear SVM: a Review,” Artif Intell Rev, vol. 52, no. 2, pp. 803–855, 2019, doi: 10.1007/s10462-018-9614-6.

L. Kong and J. Cheng, “Classification and Detection of COVID-19 X-Ray Images Based On DenseNet and VGG16 Feature Fusion,” Biomed Signal Process Control, vol. 77, Aug. 2022, doi: 10.1016/j.bspc.2022.103772.

Abbas Z, Rehman M U, Najam S, and Danish Rizvi S M, An Efficient Gray-Level Co-Occurrence Matrix (GLCM) based Approach Towards Classification of Skin Lesion. IEEE, 2019. doi: 10.1109/AICAI.2019.8701374.

A. Rasak, A. Zubair, O. A. Alo, and A. R. Zubair, “Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis Content-Based Image Retrieval System Using Combined Features of Edge, Texture and Colour View project Renewable Energy View project Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis,” International Journal of Science and Engineering Investigations, vol. 8, p. 93, 2019, [Online]. Available: www.IJSEI.com

D. M. Putriany, E. Rachmawati, and F. Sthevanie, “Indonesian Ethnicity Recognition Based on Face Image Using Gray Level Co-occurrence Matrix and Color Histogram,” IOP Conf Ser Mater Sci Eng, vol. 1077, no. 1, p. 012040, Feb. 2021, doi: 10.1088/1757-899x/1077/1/012040.

S. Singh and K. Malik, “Feature Selection and Classification Improvement of Kinnow using SVM Classifier,” Measurement: Sensors, vol. 24, Dec. 2022, doi: 10.1016/j.measen.2022.100518.

P. H. Prastyo, I. Ardiyanto, and R. Hidayat, “Indonesian Sentiment Analysis: An Experimental Study of Four Kernel Functions on SVM Algorithm with TF-IDF,” in 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICDABI51230.2020.9325685.

S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,” Electronics (Switzerland), vol. 10, no. 12, Jun. 2021, doi: 10.3390/electronics10121388.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Purnawansyah, Aji Prasetya Wibawa, Triyanna Widyaningtyas, Haviluddin, Cholisah Erman Hasihib

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.