Klasifikasi Jenis Kacang-Kacangan Berdasarkan Tekstur Menggunakan Jaringan Syaraf Tiruan
DOI:
https://doi.org/10.35143/jkt.v6i1.3546Keywords:
Classification, GLCM, ANNAbstract
Classification of types of beans is done on red beans, green beans, and peanuts. Texture features are obtained using the Gray Level Co-occurrence Matrix (GLCM) algorithm. The algorithm used to do the classification is Artificial Neural Networks (ANN). Experiments carried out with 3 different numbers of neurons in the hidden layer. Also, there are 17 types of training functions used. Each experiment scenario was repeated 5 times. Based on the experimental scenario, the best results are 99.8% for accuracy, 99.6% for precision and 99.8% for recall using 20 neurons in the hidden layer.Downloads
References
U. E. Mas’ud Effendi, Fitriyah, “Identifikasi Jenis Dan Mutu Teh Menggunakan Pengolahan Citra D igital dengan Metode Jaringan Syaraf Tiruan,†J. Teknotan, vol. 11, no. 2, pp. 67–76, 2017.
A. S. Somantri, M. Miskyah, and W. Broto, “Pendugaan tingkat keamanan jagung dengan menggunakan pengolahan citra digital dan jaringan syaraf tiruan,†J. Stand., vol. 11, no. 1, p. 27, 2009, doi: 10.31153/js.v11i1.5.
M. E. Al Rivan and T. Juangkara, “Identifikasi Potensi Glaukoma dan Diabetes Retinopati Melalui Citra Fundus Menggunakan Jaringan Syaraf Tiruan,†JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 6, no. 1, pp. 43–48, 2019, doi: 10.35957/jatisi.v6i1.158.
M. A. Agmalaro, A. Kustiyo, and A. R. Akbar, “Identifikasi Tanaman Buah Tropika Berdasarkan Tekstur Permukaan Daun Menggunakan Jaringan Syaraf Tiruan,†J. Ilmu Komput. dan Agri-Informatika, vol. 2, no. 2, p. 73, 2013, doi: 10.29244/jika.2.2.73-82.
A. Albahry and B. Kusbiantoro, “Identifikasi Varietas Berdasarkan Warna dan Tekstur Permukaan Beras Menggunakan Pengolahan Citra Digital dan Jaringan Syaraf Tiruan,†Identifikasi Var. Berdasarkan Warn. dan Tekstur Permukaan Beras Menggunakan Pengolah. Citra Digit. dan Jar. Syaraf Tiruan, vol. 32, no. 2, pp. 91–97, 2013, doi: 10.21082/jpptp.v32n2.2013.p91-97.
A. Chaugule and S. N. Mali, “Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties,†J. Eng. (United Kingdom), vol. 2014, 2014, doi: 10.1155/2014/617263.
N. V G, A. S. Kini, and A. S. Kini, “An intelligent classification model for peanut’s varieties by color and texture features,†Int. J. Eng. Technol., vol. 7, no. 2.27, p. 250, 2018, doi: 10.14419/ijet.v7i2.27.12473.
F. Wibowo and A. Harjoko, “Klasifikasi Mutu Pepaya Berdasarkan Ciri Tekstur GLCM Menggunakan Jaringan Saraf Tiruan,†Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 3, no. 2, p. 100, 2018, doi: 10.23917/khif.v3i2.4516.
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