Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine
-
Nuke L. Chusna
*
Universitas Krisnadwipayana, Jakarta Timur,
Indonesia
- Mohammad Imam Shalahudin Sekolah Tinggi Teknologi Informasi NIIT, Jakarta Selatan, Indonesia
- Umbar Riyanto Universitas Muhammadiyah Tangerang, Tangerang, Indonesia
- Allan Desi Alexander Universitas Bhayangkara Jakarta Raya, Jakarta Selatan, Indonesia (*) Corresponding Author
Abstract
Mushrooms are plants that have high nutritional content and have various benefits for the health of the human body. However, not everyone knows the types of mushrooms that are suitable for consumption. The types of mushrooms have their own characteristics when viewed from the image. For this reason, a system is needed by utilizing digital image processing to classify types of mushrooms suitable for consumption, so that people can find out which types of mushrooms are suitable for consumption. This research is to classify types of mushrooms suitable for consumption using the Multiclass SVM algorithm with first-order feature extraction, which performs feature extraction based on the characteristics of the image histogram. The result of feature extraction is used as input for classification in Multiclass SVM. Multiclass SVM can map data points to dimensionless space to obtain hyperplane linear separation between each class. The developed method is implemented in Matlab, in order to produce a system in the form of a GUI so that it can be used by general users easily. Based on the test results, the average accuracy is 83%.
Downloads
References
A. Zubair and A. R. Muslikh, “Identifikasi Jamur Menggunakan Metode K-Nearest Neighbor Dengan Ekstraksi Ciri Morfologi,” in Seminar Nasional Sistem Informasi, 2017, no. September, pp. 965–972.
R. Hanseliani, “Klasifikasi Berbagai Jenis Jamur Layak Konsumsi dengan Metode Backpropagation,” MEANS (Media Inf. Anal. dan Sist., vol. 4, no. 2, pp. 200–209, 2019.
S. Ratna, “Pengolahan Citra Digital dan Histogram Dengan Phyton dan Text Editor Phycharm,” Technologia, vol. 11, no. 3, pp. 181–186, 2020.
R. I. Borman, B. Priopradono, and A. R. Syah, “Klasifikasi Objek Kode Tangan pada Pengenalan Isyarat Alphabet Bahasa Isyarat Indonesia (Bisindo),” in Seminar Nasional Informatika dan Aplikasinya (SNIA), 2017, no. September, pp. 1–4.
P. Prasetyawan, I. Ahmad, R. I. Borman, A. Ardiansyah, Y. A. Pahlevi, and D. E. Kurniawan, “Classification of the Period Undergraduate Study Using Back-propagation Neural Network,” in Proceedings of the 2018 International Conference on Applied Engineering, ICAE 2018, 2018.
D. Alita, Y. Fernando, and H. Sulistiani, “Implementasi Algoritma Multiclass SVM Pada Opini Publik Berbahasa Indonesia di Twitter,” J. Teknokompak, vol. 14, no. 2, p. 86, 2020.
K. Meenakshi, K. Swaraja, U. K. Ch, and P. Kora, “Grading of Quality in Tomatoes Using Multi-class SVM,” in International Conference on Computing Methodologies and Communication (ICCMC 2019), 2019, pp. 104–107.
R. A. Safitri, S. Nurdiani, D. Riana, and S. Hadianti, “Klasifikasi Jenis Buah Apel Menggunakan Metode Orde 1 dengan Algoritma Multi Support-Vector Machines,” Paradig. J. Inform. dan Komput., vol. XXI, no. 2, pp. 167–172, 2019.
N. Neneng, K. Adi, and R. R. Isnanto, “Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices ( GLCM ),” J. Sist. Inf. Bisnis, vol. 1, pp. 1–10, 2016.
T. R. Pahlevi, R. Buaton, and Nurhayati, “Identifikasi Jenis Bunga Menggunakan Ekstraksi Ciri Orde Satu dan Algoritma Multi Support-Vector,” J. Inform. Kaputama, vol. 5, no. 1, pp. 116–128, 2021.
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 ICOMITEE 2021, 2021, pp. 46–50.
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 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) Identification, 2021, pp. 12–17.
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.
K. Saputra S and M. I. Perangin-Angin, “Klasifikasi Tanaman Obat Berdasarkan Ekstraksi Fitur Morfologi Daun Menggunakan Jaringan Syaraf Tiruan,” J. Inform., vol. 5, no. 2, pp. 169–174, 2018.
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.
F. Liantoni and A. A. Santoso, “Penerapan Ekstraksi Ciri Statistik Orde Pertama Dengan Ekualisasi Histogram Pada Klasifikasi Telur Omega-3,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 2, pp. 953–958, 2018.
M. A. Wani, H. F. Bhat, and T. R. Jan, “Position Specific Scoring Matrix and Synergistic Multiclass SVM for Identification of Genes,” in 17th IEEE International Conference on Machine Learning and Applications Position, 2018, pp. 1192–1196.
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.
R. I. Borman, R. Napianto, P. Nurlandari, and Z. Abidin, “Implementasi Certainty Factor Dalam Mengatasi Ketidakpastian Pada Sistem Pakar Diagnosa Penyakit Kuda Laut,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. VII, no. 1, pp. 1–8, 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine
Pages: 178−183
Copyright (c) 2022 Nuke L. Chusna, Mohammad Imam Shalahudin, Umbar Riyanto, Allan Desi Alexander
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
