Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics

  • Rini Nuraini Universitas Nasional
  • Rachmat Destriana Universitas Muhammadiyah Tangerang
  • Desi Nurnaningsih Universitas Muhammadiyah Tangerang
  • Yeni Daniarti Universitas Muhammadiyah Tangerang
  • Allan Desi Alexander Universitas Bhayangkara Jakarta Raya
DOI: https://doi.org/10.29207/resti.v7i1.4673
Keywords: histogram characteristics, first order feature extraction, support vector machine, multiclass SVM

Abstract

Sunflower is an important commodity in agriculture, besides being used as an ornamental plant, sunflower is an oil-producing plant and a source of industrial materials. In Indonesia, sunflower productivity is considered less than optimal, because knowledge and information about sunflowers are still lacking. Therefore, information is needed that can be used as an extension of knowledge about sunflowers itself, especially in Indonesia, which is a tropical region which is an area suitable for the growth of sunflowers. Sunflowers can actually be identified based on recognizable traits. However, the similar shape makes it difficult for some people to distinguish the types of sunflowers. This study aims to classify sunflower images using a first-order feature extraction algorithm using the characteristics of mean, skewness, variance, kurtosis, and entropy which are then used as input to the Multiclass SVM identification algorithm. Data points are mapped to dimensionless space using a Multiclass SVM to produce hyperplane-linear separation between each class. Based on the results of testing the accuracy of the model is able to perform classification with an average accuracy of 79%. These results show that the developed model can classify well.

 

Downloads

Download data is not yet available.

References

A. Wahyudi, M. Rahmasari, N. Nazirwan, and M. F. Sari, “Keragaman Empat Eksesi Bunga Matahari (Helianthus annuus L.) Menggunakan Penanda Morfologi,” J. Agrotek Trop., vol. 10, no. 1, pp. 103–109, 2022.

D. Ghina and N. Rahmi, “Fenologi dan Karakterisasi Morfo-Agronomi Tanaman Bunga Matahari (Helianthus annuus L.) pada Kawasan Tropis Phenology and Morpho-Agronomic Characterization in Sunflower ( Helianthus annuus L.) on Tropic Area,” J. Produksi Tanam., vol. 7, no. 5, pp. 792–800, 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, 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.

M. Bobbi, K. Nasution, S. Suryadi, and R. Watrianthos, “Model Pengenalan Suara Teks Bebas Menggunakan Algoritma Support Vector Machine,” Jurnal Media Informatika Budidarma, vol. 4, no. 4, pp. 1249–1255, 2020, doi: 10.30865/mib.v4i4.2436.

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,” 2018.

R. Rafie, “Klasifikasi Bunga Menggunakan Naïve Bayes Berdasarkan Fitur Warna Dan Texture,” J. Sains Komput. dan Teknol. Inf., vol. 4, no. 1, pp. 90–94, 2021.

N. S. B. Kusrorong, D. R. Sina, and N. D. Rumlaklak, “Kajian Machine Learning Dengan Komparasi Klasifikasi Prediksi Dataset Tenaga Kerja Non-Aktif,” J-ICON, vol. 7, no. 1, pp. 37–49, 2019.

A. P. B. Salsabila, R. D. Yunita, and C. Rozikin, “Identifikasi Citra Jenis Bunga menggunakan Algoritma KNN dengan Ekstrasi Warna HSV dan Tekstur GLCM,” Technomedia J., vol. 6, no. 1, pp. 124–137, 2021.

D. P. Pamungkas, “Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Indentifikasi Jenis Anggrek (Orchidaceae),” Innov. Res. Informatics, vol. 1, no. 2, pp. 51–56, 2019.

Y. Yuliska and K. U. Syaliman, “Peningkatan Akurasi K-Nearest Neighbor Pada Data Index Standar Pencemaran Udara Kota Pekanbaru,” IT J. Res. Dev., vol. 5, no. 1, pp. 11–18, 2020.

A. Mulyanto, R. I. Borman, P. Prasetyawan, W. Jatmiko, P. Mursanto, and A. Sinaga, “Indonesian Traffic Sign Recognition For Advanced Driver Assistent (ADAS) Using YOLOv4,” in International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2020, pp. 520–524.

A. Mulyanto, R. I. Borman, P. Prasetyawana, and A. Sumarudin, “2d lidar and camera fusion for object detection and object distance measurement of ADAS using robotic operating system (ROS),” Int. J. Informatics Vis., vol. 4, no. 4, pp. 231–236, 2020, doi: 10.30630/joiv.4.4.466.

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, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/teknokompak/article/view/792

D. A. Kumar, P. S. Chakravarthi, and K. S. Babu, “Multiclass Support Vector Machine based Plant Leaf Diseases Identification from Color, Texture and Shape Features,” in International Conference on Smart Systems and Inventive Technology (ICSSIT 2020), 2020, pp. 1220–1226.

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. doi: 10.1109/ICE3IS54102.2021.9649677.

M. Wati, Haviluddin, N. Puspitasari, E. Budiman, and R. Rahim, “First-order Feature Extraction Methods for Image Texture and Melanoma Skin Cancer Detection,” in International Conference on Mechanical, Electronics, Computer, and Industrial Technology, 2018, pp. 1–9.

I. Ahmad, Y. Rahmanto, D. Pratama, and R. I. Borman, “Development of augmented reality application for introducing tangible cultural heritages at the lampung museum using the multimedia development life cycle,” Ilk. J. Ilm., vol. 13, no. 2, pp. 187–194, 2021.

D. Dahliyusmanto, D. W. Anggara, M. S. M. Rahim, and A. W. Ismail, “The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 5, pp. 2104–2111, 2021.

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.

Z. Abidin, R. I. Borman, F. B. Ananda, P. Prasetyawan, F. Rossi, and Y. Jusman, “Classification of Indonesian Traditional Snacks Based on Image Using Convolutional Neural Network (CNN) Algorithm,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2021, pp. 18–23. doi: 10.1109/ICE3IS54102.2021.9649707.

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.

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, doi: 10.24176/simet.v9i2.2476.

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. doi: 10.1109/ICMLA.2018.00192.

S. Chakraborty, S. Paul, and M. Rahat-uz-Zaman, “Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine,” in International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), 2021, pp. 147–151.

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, Y. Fernando, and Y. Egi Pratama 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, doi: 10.29207/resti.v6i2.3954.

Published
2023-02-03
How to Cite
Nuraini, R., Destriana, R., Nurnaningsih, D., Daniarti, Y., & Desi Alexander, A. (2023). Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(1), 146 - 152. https://doi.org/10.29207/resti.v7i1.4673
Issue
Vol 7 No 1 (2023): February 2023
Section
Information Technology Articles

Copyright (c) 2023 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Creative Commons License

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

Copyright in each article belongs to the author

  1. The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
  2. Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;