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Idham Triatmaja
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sistem Diagnosis Penyakit Sapi Menggunakan Metode Neighbor Weighted K-Nearest Neighbor Berbasis Android Idham Triatmaja; Nurul Hidayat; Moch Cholil Mahfud
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Problems in raising cattle is infection of disease. Infection of diseases that arise in each cow can be different so that required accuracy in making the diagnosis (Pambudi, 2010). Cattle ranchers are sometimes difficult to know cow disease due to the limited knowledge of breeders of cattle disease. The difficulty of seeking medical personnel such as veterinarians becomes a problem for farmers because they can not quickly handle cows affected by the disease. When cattle are infected with disease, experts / experts such as veterinarians are very necessary in overcoming it. Based on the explanation of the problem it will be designed an system by combining the object of cow disease research and the NWK-NN method. The title of the study was "System of Cow Disease Diagnosis with Neighbors Weighted K-Nearest Neighbors Method based on Android". There are 11 types of cow disease including Abscess, Ascariasis, Bloat, Bovine Ephemeral Fever (BEF), Endometritis, Entritis, Mastitis, Omphalitis, Pneumonia, Rentensio, and Scabies. This cow disease diagnosis system has the main process of calculating Euclidean distance, determining the data of a number k of neighborhoods, determining the membership value of each data for each class determining the membership value of each class and determining the largest membership value. The variable values ​​of k=5, 10, 15, 20 and 25 have an average accuracy of 97.56% while for the value of k>25 has a decreasing average accuracy. The stable average accuracy is k with values ​​5, 10, 15, 20 and 25 with an accuracy of 97.56%.
Co-Authors Moch Cholil Mahfud Nurul Hidayat