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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Dizka Maryam Febri Shanti
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Metode F-KNN (Fuzzy K-Nearest Neighbor) Untuk Diagnosis Penyakit Anjing Dizka Maryam Febri Shanti; Nurul Hidayat; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Dogs are one of the favorite animals used as pets. When petting and playing with dogs, oxytocin, stress-related and relieved hormones are released, helping to lower blood pressure as well as cortisol levels. Although dog maintenance has many benefits, the owners should be careful in caring for their dogs. Not a few dogs are attacked by various diseases caused by viruses, protozoa, bacteria and parasites. Dogs who are sick if not immediately get treatment and treatment have the risk of transmitting to dogs and other animals or even to humans. The method used Fuzzy K-Nearest Neighbor. (FK-NN) is a variant of K-Nearest Neighbor (K-NN) method with fuzzy technique. The FK-NN method assigns a class membership value to the sample vector instead of placing the vector in a particular class. FK-NN can be implemented for the diagnosis of diseases in dogs by several stages: calculating the distance between the train data and the test data, taking the smallest distance between the train data and the test data as much as K, Fuzzification and Defuzzification, Class with the highest defuzzification value used as the class for the result classification. The value of K affects the accuracy of the system where the higher the value of k then the tendency of accuracy will decrease. The highest accuracy obtained from the test results is when K = 5 ie with a value of 98.67%.
Co-Authors Nurul Hidayat Randy Cahya Wihandika