Deteksi Mata Katarak Berdasarkan Tekstur Gray Gray Level Co-Ocurrence Matrix Dengan Metode Self Organizing Map
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Abstract
Cataracts are a common eye problem in Indonesia. Untreated cataracts are the main cause of blindness and the most dominant vision impairment in Indonesia among people over 50 years old, with a proportion reaching 77.7%. Regular eye examinations are necessary to prevent cataracts, but there are obstacles due to the availability of equipment and the cost of eye examinations. Therefore, an efficient and effective cataract detection system is needed. This study aims to detect cataracts in the eyes by utilizing a dataset of eyes from the internet. This research uses RGB and HSV feature extraction combined with the GLCM extraction method. The Gray Level Co-occurrence Matrix (GLCM) method describes the spatial relationship between pixel intensities in an image. With the GLCM matrix, texture information can be extracted from eye images. The Self Organizing Map (SOM) method performs learning based on feature extraction obtained from previously labeled eye images. Based on the test results, this study successfully detects cataract and normal eyes. With the greatest accuracy achieved in the comparison of 80% training data and 20% test data with dimension size = [2 2] and maximum iterations of 100, the highest accuracy of 90% was obtained.
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