Deteksi Penyakit Kucing dengan Menggunakan Modified K-Nearest Neighbor Teroptimasi (Studi Kasus: Puskeswan Klinik Hewan dan Satwa Sehat Kota Kediri) - Fitri Dwi Astuti; Dian Eka Ratnawati; Agus Wahyu Widodo (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Cats are animals that are widely nurtured by people, so there are now many findings related to cat disease caused by many factors. Knowledge and understanding of the symptoms that occur in the cat to be an important factor, so that people can better anticipate the occurrence of more severe disease. With some of the problems that have been described before then give the idea to built an application "Deteksi Penyakit Kucing". In this study the method used is Modified K-Nearest Neighbor, but the method has a weakness in the biased k value, so the accuracy of the resulting level sometimes less than the maximum. Given the problem, the genetic algorithm is used to optimize k value in the Modified K-Nearest Neighbor method. Data used in this research is cat disease data at Puskeswan Klinik Hewan dan Satwa Sehat of Kediri with amount of training data as many as 105 and test data counted 35. From all data will be classified into 7 class with criterion as much as 19. Accuracy result of Modified K-Nearest Neighbor using genetic algorithm for optimal k 1 is 100%. From these results the application of cat disease detection with optimal k value can be used by the public to recognize diseases in cats.