Comparison of Extreme Learning Machine (ELM) and Multi-Support Vector Machine (Multi-SVM) Methods in Herbal Plants Identification

Luluk Sarifah; Lailiyatus Sa’adah; Iis Setiana

doi:10.20956/j.v21i2.37107

Authors

Luluk Sarifah Universitas Annuqayah
Lailiyatus Sa’adah Universitas Annuqayah
Iis Setiana Universitas Annuqayah

DOI:

https://doi.org/10.20956/j.v21i2.37107

Keywords:

Identification, Comparison, Herbal Plants, ELM, Multi-SVM

Abstract

In Indonesia, there are more than 2.039 species of herbal medicinal plants, which sometimes have similarities and make it difficult to identify the type of herbal plant. The purpose of this study is to facilitate the identification of herbal plant species by comparing the performance of the Extreme Learning Machine (ELM) and Multi-Support Vector Machine (Multi-SVM) methods. The ELM method was created to overcome the weaknesses of feedforward artificial neural networks, especially in terms of learning speed, while the Multi-SVM method is an advanced development of the SVM method. The stages of this research begin with image input which is through previous data acquisition, data preprocessing, and then the identification with ELM and Multi-SVM methods. Based on the simulations that have been carried out, the average accuracy on training data for the ELM method is 93%, while the Multi-SVM method is 44%. Also, the average accuracy on testing data for the ELM method is 85%, while the Multi-SVM method is 40%.

References

[1] Bishop, C., M., 2006. A Review of Pattern Recognition and Machine Learning, Springer, https://doi.org/10.53759/7669/jmc202404020.

[2] Borman, R., I., Rossi, F. & Jusman, Y., Rahni, A., A., A, Putra, S., D. & Herdiansah, A., 2021. Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm. 2021 1st International Conference Electronic and Electrical Engineering and Intelligent System (ICE3IS). 1230, 12–17. https://doi.org/10.1109/ice3is5410

[3] Fikriya, Z., A., Irawan, M., I. & Soetrisno, S., 2017. Implementasi Extreme Learning Machine untuk Pengenalan Objek Citra Digital. Jurnal Sains dan Seni ITS, Vol. 6, No. 1, https://doi.org/10.12962/j23373520.v6i1.21754.

[4] Haryono., Anam, A. & Saleh, A., 2020. Autentikasi Daun Herbal Menggunakan Convolutional Neural Network dan Raspberry Pi. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, Vol. 9, No. 3, 278-286. https://doi.org/https://doi.org/10.22146/.v9i3.302.

[5] Hilmiyah, F., 2017. Prediksi Kinerja Mahasiswa Menggunakan Support Vector Machine untuk Pengelola Program Studi di Perguruan Tinggi (Studi Kasus: Program Studi Magister Statistika ITS). Tesis. Departemen Manajemen Teknologi Bidang Keahlian Manajemen Teknologi Informasi Fakultas Bisnis Dan Manajemen Teknologi Institut Teknologi Sepuluh Nopember, Surabaya

[6] Huang, G., Zhu, Q. & Siew, C., 2004. Extreme learning machine: a new learning scheme of feedforward neural networks, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Vol. 2, 985-990, https://doi.org/10.1109/ijcnn.2004.1380068

[7] Huang, G., Zhu, Q. & Siew, C., 2006. Extreme learning machine: Theory and applications. Neurocomputing, Vol. 70, 489–501. https://doi.org/10.1016/j.neucom.2005.12.126.

[8] Huérfano-Maldonado, Y., Mora, M., Vilches, K., Hernández-García, R., Gutiérrez, R., & Vera, M., 2023. A comprehensive review of extreme learning machine on medical imaging. Neurocomputing, Vol. 556, 126618. https://doi.org/10.1016/j.neucom.2023.126618

[9] Ifandi, S., Jumari. & Widodo, S., W., A., 2015. Keanekaragaman Jenis Tumbuhan Obat Masyarakat Suku Kaili di Dusun Tompu Kecamatan Sigi Biromaru Kabupaten Sigi Sulawesi Tengah. Prosiding Seminar Nasional Biologi II, 27–31.

[10] Jeon, W., S. & Rhee, S., Y., 2017. Plant Leaf Recognition Using A Convolution Neural Network. International Journal of Fuzzy Logic and Intelligent Systems, Vol 17, No. 1, 26–34. https://doi.org/10.5391/IJFIS.2017.17.1.26

[11] Kaya, A., Keceli, A., S., Catal, C., Yalic, H., Y., Temucin, H. & Tekinerdogan B., 2019. Analysis Of Transfer Learning For Deep Neural Network Based Plant Classification Models. Computurs and Electronics in Agriculture, 20-29. https://doi.org/10.1016/j.compag.2019.01.041.

[12] Kemenkes RI. 2015. 100 Top tanaman obat indonesia. Badan Penelitian dan Pengembangan Kesehatan, Jakarta.

[13] Mercaldo, F., Brunese, L., Martinelli, F., Santone, A. & Cesarelli, M., 2023. Experimenting with Extreme Learning Machine for Biomedical Image Classification. Applied Sciences. https://doi.org/10.3390/app13148558

[14] Mustafa, M., S., Husin, Z., Tan, W., K., Mavi, M., F. & Farook, R., S., M., 2020. Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-019-04634-7.

[15] Pamungkas, A. Klasifikasi Jenis Kendaraan Menggunakan Algoritma Extreme Learning Machine. Pemrograman Matlab, https://pemrogramanmatlab.com/2019/04/03/klasifikasi-jenis-kendaraan-menggunakan-algoritma-extreme-learning-machine/ [14 Juni 2024].

[16] Putra, A., P., Mulyana, I., Maryana, S. & Susanti, F. 2019. Implementasi Multiclass Support Vector Machine Pada Sistem Rekomendasi Obat Berdasarkan Gejala Penyakit. Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO AAU), Vol. 1, No. 1, 211-222.

[17] Rajani, S. & Veena, V., M., 2018. Study on Identification and Classification of Medicinal Plants. International Journal of Advances in Science Engineering and Technology, Vol. 6, 13-18.

[18] Rumandan, R., J., Nuraini, R., Sadikin, N. & Rahmanto, Y., 2022. Klasifikasi Citra Jenis Daun Berkhasiat Obat Menggunakan Algoritma Jaringan Syaraf Tiruan Extreme Learning Machine. Journal of Computer System and Informatics (JoSYC), Vol. 4, No. 1, 145-154. https://doi.org/10.47065/josyc.v4i1.2586.

[19] Saputra, S., K. & Perangin-Angin, M., I., 2018. Klasifikasi Tanaman Obat Berdasarkan Ekstraksi Fitur Morfologi Daun Menggunakan Jaringan Syaraf Tiruan. Jurnal Informatika, Vol. 5, No. 2:169–174. https://doi.org/10.31311/ji.v5i2.3770.

[20] Sarifah, L., Khotijah, S. & Khaliqah, M., 2023. Identification Of Hijaiyah Letters Image Using Extreme Learning Machine Method. Jurnal Matematika, Statistika dan Komputasi, Vol. 20, No. 1, 90-101. https://doi.org/10.20956/j.v20i1.27158.

[21] Sari, W., S., & Sari, C., A., 2023. Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu. InComTech: Jurnal Telekomunikasi dan Komputer, Vol. 13, No. 1, 18-27. https://doi.org/10.22441/incomtech.v13i1.15012.

[22] Setiyono, B., Arif, M., R., Aini, Q., Q., Soegianto, T., H., Ohanna, J., Gunawan, R., A., F. & Rizkia, A., P., 2013. Identifikasi Tanaman Obat Indonesia Melalui Citra Daun Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), Vol. 10, No. 2, 385-392. https://doi.org/10.25126/jtiik.20231026809.