DOI: https://doi.org/10.14710/jtsiskom.6.4.2018.146-151

Identifikasi Jenis Bambu Berdasarkan Tekstur Daun dengan Metode Gray Level Co-Occurrence Matrix dan Gray Level Run Length Matrix

Identification of Bamboo Species Based on Leaf Texture using Gray Level Co-Occurrence Matrix and Gray Level Run Length Matrix

*Endina Putri Purwandari  -  Department of Informatics, Universitas Bengkulu, Indonesia
Rachmi Ulizah Hasibuan  -  Department of Informatics, Universitas Bengkulu, Indonesia
Desi Andreswari  -  Department of Informatics, Universitas Bengkulu, Indonesia
Received: 18 Jul 2018; Published: 31 Oct 2018.
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Abstract
Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.
Keywords: bamboo identification; bamboo leaf; grey level co-occurrence matrix; gray level run length matrix
Funding: Department of Informatics, Universitas Bengkulu

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  1. E. P. Purwandari, A. P. Yani, R. Sugraha, K. Anggriani, and E. W. Winarni, “Online Expert Systems for Bamboo Identification Using Case Based Reasoning,” International Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2776–2772, 2017
  2. A. P. Yani, “Keanekaragaman Dan Populasi Bambu Di Desa Talang Pauh Bengkulu Tengah,” Exacta, vol. X, no. 1, pp. 61–70, 2012
  3. A. Rakhmadi, N. Suciati, and A. Navastara, “Fitur Berbasis Fraktal dari Koefisien Wavelet untuk Klasifikasi Citra Daun,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 15, no. 2, pp. 238–247, 2017
  4. E. P. Purwandari, Konsep dan Teori Pengolahan Citra Digital. Bengkulu: UNIB Press, Universitas Bengkulu, 2018
  5. A. Kadir and A. Susanto, Teori dan Aplikasi Pengolahan Citra Digital. Yogyakarta: Penerbit Andi, 2013
  6. F. Shofrotun, T. Sutojo, D. R. Ignatius, and M. Setiadi, “Identifikasi Tumbuhan Obat Herbal Berdasarkan Citra Daun Menggunakan Algoritma Gray Level Co-occurence Matrix dan K-Nearest Neighbor,” Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 2, pp. 51–56, 2018
  7. S. Saifudin and A. Fadlil, "Sistem Identifikasi Citra Kayu Berdasarkan Tekstur Menggunakan Gray Level Coocurrence Matrix (GLCM) Dengan Klasifikasi Jarak Euclidean," Sinergi: Jurnal Teknik Mercu Buana, vol. 19, no. 3, pp. 181-186, 2015
  8. R. K. Dewi and R. H. Ginardi, "Identifikasi Penyakit pada Daun Tebu dengan Gray Level Co-Occurrence Matrix dan Color Moments," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 2, pp. 70-77, 2014
  9. R. A. Asmara, D. Puspitasari, S. Romlah, Q. Hasanah, and R. Romario. "Identifikasi Kesegaran Daging Sapi Berdasarkan Citranya dengan Ekstraksi Fitur Warna dan Teksturnya Menggunakan Metode Gray Level Cooccurrence Matrix," SENTIA, vol. 9, pp. 89-94,2017
  10. A. Halim, Hardy, and Mytosin, “Aplikasi Image Retrieval dengan Histogram Warna dan Multi-scale GLCM,” JSM (Jurnal SIFO Mikroskil), vol. 16, no. 1, pp. 41–50, 2015
  11. I. Santoso, Y. Christyono, and M. Indriani, “Kinerja Pengenalan Citra Tekstur Menggunakan Analisis Tekstur Metode Run Length,” in Seminar Nasional Aplikasi Teknologi Informasi, Jun 16, 2007, Yogyakarta, Indonesia, pp. 19–25
  12. I. Setiawan, K. Dedi, S. T. Rasmana, and M. C. Wibowo. "Analisis Fitur Citra Prasasti Logam Menggunakan Metode Gray Level Run Length Matriks (GLRLM),” Journal JCONES, vol. 4, no. 1, pp. 22-30, 2015
  13. B. Aditya, A. Hidayatno, and A. A. Zahra, “Sistem Pengenalan Buah Menggunakan Metode Discrete Cosine Transform dan Euclidean Distance,” Transient, vol. 3, no. 2, pp. 134–138, 2014

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