Penerapan Metode Fuzzy K-Nearest Neighbour (FK-NN) Untuk Diagnosis Penyakit Pada Kucing - Hardyan Zalfi; Agus Wahyu Widodo; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Cats are the animals most loved by humans with a very nice shape and fur many people make cats as their pets especially in Indonesia or the world. The population of cats is 220 million in the world. With the big populatian of cats, of course there are also many cats that have poor health with disease. The limited ability of a person to detect cat disease and the limited of veterinary experts requires a system that can diagnose disease in cats easily. The making of this cat disease diagnosis system uses the k-nearest neighbor fuzzy method which is a development of the k-nearest neighbor method where the membership value of the k-nearest neighbor results will be calculated. Based on the functional tests that have been carried out, each "test class produces conformity to system requirements. The first accuracy testing is testing accuracy based on variations in the amount of training data with a different amount of training data for each test. For this test the highest accuracy value obtained by 85% while the lowest accuracy value is 80%. The second accuracy testing is accuracy testing based on the influence of K values ​​with the same test data totaling 15 test data. The results of this test the greatest accuracy is 86% while the lowest is equal to 73%. This shows that the k-nearest neighbor fuzzy method has a pretty good accuracy to diagnose diseases in cats.