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.
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Original Research Articles
Language: ID
In Volume 6, Issue 4, Year 2018 (October 2018)
Statistics: 624 505
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

Article Metrics:

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