Implementasi Metode Gray Level Co-occurrence Matrix dalam Identifikasi Jenis Daun Tengkawang
Tengkawang or known as Borneo tallow nut is now difficult to find due to unsustainable forestry practices and high levels of forest destruction. The method used in this research was pattern recognition. The process of identifying Tengkawang plants was carried out through this process: image acquisition, image cutting and background removal of tengkawang leaf image, RGB image conversion of tengkawang leaf into gray scale, threshold limit determination with certain value, feature extraction with GLCM method (spatial distance 1, 2, and 3 pixels), and morphology, so the pattern of Tengkawang leaf image could be obtained. The image pattern was classified using Back Propagation Neural Network algorithm. The output is a software for identifying the types of tengkawang leaf. The results of identification testing of non-tengkawang leaf species show that using a total of 16 random samples of test images, an accuracy of 87.5% is obtained. The identification rate of tengkawang leaf image with spatial distance of 1, 2, and 3 pixels from total 24 random sample of test image shows 100% accuracy level. Training with 2 pixel spatial spacing has the lowest iteration, i.e. 10 iterations. The result of identifying damaged tengkawang leaf image on the edge has an effect on the extraction of morphological characteristics.
 T. C. Whitmore, Tropical Rain Forests of The Far East, Clarendon Press, Oxford, 1984.
 I. G. N. Tantra, “Tengkawang : a newly cultivated forest,” Plant. Ind. Agric. Research and Development, vol. 3, hal. 29-31, 1981.
 S. Russell, & P. Norvig, Artificial Intelligence a Modern Approach, 3rd Edition, New Jersey (USA): Prentice Hall, 2010.
 B. Coppin, Artificial Intelligence Illuminated, London: Jones Ana Bartlett Publishers International, 2004.
 R. C. Gonzales & R. E. Woods, Digital Image Processing, Second edition, New Jersey (USA): Prentice Hall, 2002.
 I.P.G. Budisanjaya, “Identifikasi nitrogen dan kalium pada daun tanaman sawi hijau menggunakan matriks co-occurrence, moments dan jaringan syaraf tiruan”, M.T, thesis, Universitas Udayana, Bali, 2013.
 C. Solomon & T. Breckon, Fundamentals of Digital Image Processing A Practical Approach with Examples in Matlab, USA: John Wiley & Sons Ltd, 2011.
 S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, dan Q. L. Xiang, “A leaf recognition algorithm for plant classification using probabilistic neural network: signal processing and information technology,” IEEE International Symposium, 2007, hal. 11-16.
 R. M. Haralick, K. Shanmugam, dan I. Dinstein, “Textural Features For Image Classification”, IEEE Transaction On System Man and Cybernetics, vol. 3, no. 6, hal. 25-31, 1973.
 F. Albregtsen, “Statistical texture measures computed from gray level coocurrence matrices,” Image Processing Laboratory, Department of Informatics, University of Oslo, 2008.
 T. W. A. Putra., “Pengenalan wajah dengan matrik kookurensi aras keabuan dan jaringan syaraf probabilistik,” M.Kom, thesis, Universitas Diponegoro, Semarang, 2013.
 Lai & H. Hsin, “IDD: a case based model of learning in design using artificial neural network-based approach,” International Journal of Computer Science and Network Security, vol. 6, hal. 242-246, 2006.
 J. J. Siang, Jaringan Syaraf Tiruan & Pemrogramannya Menggunakan Matlab, Yogyakarta: C.V. Andi offset, 2009.
 A. Hermawan, Jaringan Saraf Tiruan (Teori dan Aplikasi), Yogyakarta: C.V. Andi offset, 2006.
 R. Maharani, P. Handayani, dan A. K. Hardjanan, “Panduan identifikasi jenis pohon tengkawang,” Balai Besar Penelitian Dipterokarpa Departemen Kehutanan, Kaltim, 2013.