Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

Chrisani Waas (Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura)
D. L. Rahakbauw (Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura)
Yopi Andry Lesnussa (Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura)

JAIS (Journal of Applied Intelligent System)
Vol 4, No 2 (2019): Journal of Applied Intelligent System

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.