Klasifikasi Penyakit Mata Katarak berdasarkan Kelainan Patologis dengan menggunakan Algoritma Learning Vector Quantization

Abstract

Cataract is one type of eye damage which causes the lens of the eye, nearsightedness which varies according to the level becomes blindness. Cataract eye disease is eating slowly, little by little without the pain experienced by patients but if handled too late then lead to permanent blindness. Eyepiece contains 65% water, 35% protein and the rest are minerals. With increasing age, size and mineral density increases. The accuracy of the determination of the type and location of early cataract is very important to prevent the severity of the impact of more severe cataracts. The main procedure of cataract diagnosis (Gold Standard procedure) was performed using computed tomography (CT) scan and Magnetic Resonance Imaging (MRI). Alternative diagnosis can be made through physical examination, laboratory tests, medical history, and other relevant information. The purpose of this paper presents the results of a study on the implementation of the method of Learning Vector Quantization (LVQ) to facilitate the determination of the classification of types of cataract disease and its severity. The results showed that the use LVQ provide the level of accuracy of the determination of the amount of (99%) as well as the duration of training (training) sample of (0.06 seconds).