Klasifikasi Penyakit Kulit Kucing menggunakan Metode Support Vector Machine - Yuwilda Wilantikasari; Imam Cholisoddin; Edy Santoso (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Cats are one of the most popular pets in the world. However, health is a matter of concern in the nurturing of cats. Indonesia has a relatively high humidity of air, hence parasites and fungi can multiply and spread which could cause skin diseases. The scarce knowledge of cat owners about cat skin disease and some symptoms that have similarities to various types of cat skin disease are difficult to identify. With these problems, an intelligent system that can classify cat skin diseases based on symptoms is proposed. This intelligent system also aims to help medical teams especially in the field of veterinary medicine in providing a diagnosis of cat skin diseases. Support Vector Machine method can be applied to skin disease classification problems using a limited dataset of 240 with 14 parameter. This study uses five classes of classes: scabies, cat flea, abscesses, dermatitis, and fungi. SVM performances gave an accuracy of 98.745% with parameter value on sequential training SVM, , y = 0.01, C = 10, = 0.01, iteration = 100 and the ratio data of 90%:10%.