Classification of Medicinal Wild Plant Leaf Types Using a Combination of ELM and PCA Algorithms

 (*)Dedy Alamsyah Mail (Universitas Muhammadiyah Tangerang, Tangerang, Indonesia)
 Farli Rossi (Universiti Kebangsaan Malaysia, Selangor, Malaysia)
 Ri Sabti Septarini (Universitas Muhammadiyah Tangerang, Tangerang, Indonesia)
 Mohammad Imam Shalahudin (Sekolah Tinggi Teknologi Informasi NIIT, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: September 20, 2023; Published: October 25, 2023

Abstract

Despite their detrimental nature, it turns out that wild plants have many benefits for human health. Wild plants with a form of herbaceous vegetation contain ingredients that can be used as medicine, especially in their leaves. However, because the information is very similar and the form is similar, people don't know about it. For this reason, the aim of this research is to implement an artificial neural network algorithm using Extreme Learning Machine (ELM) and the Principal Component Analysis (PCA) algorithm to classify images of wild plant leaves with medicinal properties, especially in herbaceous vegetation. The feature extraction used in this research involves morphological features by considering the shape of the object. The PCA algorithm will reduce data complexity and identify hidden patterns in the data by changing the original feature space to a new and more concise feature space. Next, the ELM algorithm is used to recognize class grouping patterns when solving classification problems. Accuracy test results show a value of 90.667%.

Keywords


Extreme Learning Machine; ELM; Morphological Features; Principal Component Analysis; PCA

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References

R. Rizki, N. Nursyahra, and O. Fernando, “Study of Weeds as Traditional Medicinal Plants Used by Indigenous People of West Pasaman, Indonesia,” J. Trop. Hortic., vol. 2, no. 2, pp. 81–85, 2019, doi: 10.33089/jthort.v2i2.21.

P. Gusain, D. P. Uniyal, and R. Joga, “Chapter 23 - Conservation and sustainable use of medicinal plants,” C. Egbuna, A. P. Mishra, and M. R. B. T.-P. of P. for the M. of D. Goyal, Eds. Academic Press, 2021, pp. 409–427. doi: https://doi.org/10.1016/B978-0-12-820284-5.00026-5.

E. B. Minarno, A. Rubani, and M. R. Ridho, “Ethnobotany of Medicinal Wild Plants in the Community of Kutorejo Subvillage, Buffer Area of Alas Purwo National Park Banyuwangi Regency,” in Proceedings of the 12th International Conference on Green Technology (ICGT), 2022, pp. 110–117. doi: 10.2991/978-94-6463-148-7_12.

E. R. Dougherty, Digital Image Processing Methods. New York: CRC Press, 2020.

H. Mayatopani, R. I. Borman, W. T. Atmojo, and A. Arisantoso, “Classification of Vehicle Types Using Backpropagation Neural Networks with Metric and Ecentricity Parameters,” J. Ris. Inform., vol. 4, no. 1, pp. 65–70, 2021, doi: 10.34288/jri.v4i1.293.

R. I. Borman, F. Rossi, Y. Jusman, A. A. A. Rahni, S. D. Putra, and A. Herdiansah, “Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2021, pp. 12–17.

M. Zeeshan, A. Prabhu, C. Arun, and N. S. Rani, “Fruit Classification System Using Multiclass Support Vector Machine Classifier,” in Proceedings of the International Conference on Electronics and Sustainable Communication Systems, 2020, pp. 289–294.

H. Mayatopani, N. Handayani, R. S. Septarini, R. Nuraini, and N. Heriyani, “Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 3, pp. 437–444, 2023.

I. Ahmad, Y. Rahmanto, R. I. Borman, F. Rossi, Y. Jusman, and A. D. Alexander, “Identification of Pineapple Disease Based on Image Using Neural Network Self-Organizing Map (SOM) Model,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022.

R. I. Borman, I. Ahmad, and Y. Rahmanto, “Klasifikasi Citra Tanaman Perdu Liar Berkhasiat Obat Menggunakan Jaringan Syaraf Tiruan Radial Basis Function,” Bull. Informatics Data Sci., vol. 1, no. 1, pp. 6–13, 2022.

R. I. Borman, F. Rossi, D. Alamsyah, R. Nuraini, and Y. Jusman, “Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022.

A. Toprak, “Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer,” Med. Sci. Monit., vol. 24, pp. 6537–6543, 2018, doi: 10.12659/MSM.910520.

J. P. Tanjung and B. A. Wijaya, “Facial Recognition Implementation using K-NN and PCA Feature Extraction in Attendance System,” Sink. J. dan Penelit. Tek. Inform., vol. 5, no. 1, pp. 43–50, 2020.

I. Ahmad, E. Suwarni, R. I. Borman, A. Asmawati, F. Rossi, and Y. Jusman, “Implementation of RESTful API Web Services Architecture in Takeaway Application Development,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022, pp. 132–137. doi: 10.1109/ICE3IS54102.2021.9649679.

A. Mulyanto, W. Jatmiko, P. Mursanto, P. Prasetyawan, and R. I. Borman, “A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS,” J. ICT Res. Appl., vol. 14, no. 3, pp. 286–298, 2021.

U. Erkut, F. Bostancıoğlu, M. Erten, A. M. Özbayoğlu, and E. Solak, “HSV Color Histogram Based Image Retrieval with Background Elimination,” in International Informatics and Software Engineering Conference (UBMYK), 2019.

R. I. Borman, Y. Fernando, and Y. E. P. Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 2, pp. 339–345, 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 Ris. Komputer), vol. 9, no. 2, pp. 388–395, 2022, doi: 10.30865/jurikom.v9i1.3846.

A. Das, S. Agrawal, L. Samantaray, R. Panda, and A. Abraham, “State-of-the Art Optimal Multilevel Thresholding Methods for Brain MR Image Analysis,” Int. Inf. Eng. Technol. Assoc., vol. 34, no. 3, pp. 243–256, 2020.

R. Nuraini, “Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operations,” IJICS (International J. Informatics Comput. Sci., vol. 6, no. 2, pp. 89–96, 2022, doi: 10.30865/ijics.v6i2.4499.

R. I. Borman, Y. Fernando, and Y. E. P. Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 158, pp. 339–345, 2022.

M. A. Hasan and A. S. Budi, “Pears Classification Using Principal Component Analysis and K-Nearest Neighbor,” Sink. J. dan Penelit. Tek. Inform., vol. 4, no. 2, pp. 34–41, 2020.

J. S. Manoharan, “Study of Variants of Extreme Learning Machine (ELM) Brands and its Performance Measure on Classification Algorithm,” J. Soft Comput. Paradig., vol. 03, no. 02, pp. 83–95, 2021.

D. Zabala-blanco, M. Mora, R. Hernandez-Garcıa, and R. J. Barrientos, “The Extreme Learning Machine Algorithm for Classifying Fingerprints,” in 39th International Conference of the Chilean Computer Science Society (SCCC), 2020. doi: https://doi.org/10.1109/SCCC51225.2020.9281232.

D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion matrix,” Comput. Oper. Res., vol. 152, p. 106131, 2023, doi: https://doi.org/10.1016/j.cor.2022.106131.

S. Al-hammouri, M. Fora, and M. Ibbini, “Extreme Learning Machine for Melanoma Classification,” in Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) 2021, 2021, pp. 114–119.

J. Wang, S. Lu, S.-H. Wang, and Y.-D. Zhang, “A review on extreme learning machine,” Multimed. Tools Appl., vol. 81, no. 29, pp. 41611–41660, 2022, doi: 10.1007/s11042-021-11007-7.

F. Mercaldo, L. Brunese, F. Martinelli, A. Santone, and M. Cesarelli, “Experimenting with Extreme Learning Machine for Biomedical Image Classification,” Appl. Sci., vol. 13, no. 14, 2023, doi: 10.3390/app13148558.

M.-T. Wu, “Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom,” Sci. Rep., vol. 12, no. 1, p. 3095, 2022, doi: 10.1038/s41598-022-07137-z.

R. I. Borman, R. Napianto, N. Nugroho, D. Pasha, Y. Rahmanto, and Y. E. P. Yudoutomo, “Implementation of PCA and KNN Algorithms in the Classification of Indonesian Medicinal Plants,” in International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), 2021, pp. 46–50. doi: 10.1109/ICOMITEE53461.2021.9650176.

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