Implementasi Algoritma K-Nearest Neighbor Untuk Klasifikasi Deteksi Penyakit Pada Anjing - Richard Emmanuel Johanes; Edy Santoso; Suprapto Suprapto (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2020)
Dogs are one of the pets that are loved in any part of the world. In Indonesia, dogs have started to be in great demand by some communities to become pets. Dogs themselves, including pets, are classified as expensive because of the cost of care. The dog itself is also susceptible to disease when the care and supervision of its care. There are limited veterinary clinics and there are still very few people who want to bring their pets to the doctor when they are sick. Therefore we need a system that can help all dog owners to detect early what kind of disease the dog is experiencing through the symptoms experienced by the dog so that early subscription can be done quickly. To solve this problem, an expert system is needed to help diagnose dog diseases using the K-Nearest Neighbor (KNN) algorithm classification method. Implementation of the system using the Java programming language by using training data of 5 diseases with 23 symptoms. Testing is done by using the accuracy equation test using 50 samples of test data resulting in an accuracy of 88%.