Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN)

Muh Nasirudin Karim(1*); Ricardus Anggi Pramunendar(2); Moch Arief Soeleman(3); Purwanto Purwanto(4); Bahtiar Imran(5);

(1) Universitas Dian Nuswantoro
(2) Universitas Dian Nuswantoro
(3) Universitas Dian Nuswantoro
(4) Universitas Dian Nuswantoro
(5) Universitas Teknologi Mataram
(*) Corresponding Author



This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.


Pearl Image; Image Classification; GLCM; ANN


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