APLIKASI DIAGNOSA RABUN MATA MENGGUNAKAN METODE K-NEAREST NEIGHBOUR BERDASARKAN GRAY LEVEL CO-OCCURRENCE MATRICES (GLCM)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Nur, Ocha Hanif
Jatnika, Hendra
Agtriadi, Herman Bedi
Jatnika, Hendra
Agtriadi, Herman Bedi
Subject
Teknik Informatika
Datestamp
2022-09-21 01:39:26
Abstract :
Farsightedness is one eye refraction disease that often occurs by humans. Farsightedness can be identified by examining the optic area of the retinal fundus of the eye. As a result of busyness from an ophthalmologist to analyze the fundus image makes treatment of eye disease to patients can not be done quickly. This writing focuses on how to identify myopic eyes by using fundus images suffering from hypermetropy and myopia. The process for identifying fundus images is carried out using the resize, greyscale and image classification processes. To extract features using the Gray Level Co-occurrence Matrix (GLCM) method. By using the GLCM method, we will get a cohesion matrix which then look for features that are contrast, energy, correlation and homogeneity. While the classification process used is the KNearest Neighbor method. The accuracy calculation in this study uses a matrix confusion. Where the results obtained by 70%
Farsightedness is one eye refraction disease that often occurs by humans. Farsightedness can be identified by examining the optic area of the retinal fundus of the eye. As a result of busyness from an ophthalmologist to analyze the fundus image makes treatment of eye disease to patients can not be done quickly. This writing focuses on how to identify myopic eyes by using fundus images suffering from hypermetropy and myopia. The process for identifying fundus images is carried out using the resize, greyscale and image classification processes. To extract features using the Gray Level Co-occurrence Matrix (GLCM) method. By using the GLCM method, we will get a cohesion matrix which then look for features that are contrast, energy, correlation and homogeneity. While the classification process used is the KNearest Neighbor method. The accuracy calculation in this study uses a matrix confusion. Where the results obtained by 70%
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