Optimalisasi Algoritma C4.5 dalam Menganalisis Indikasi Penyebab Penyakit Feline Immunodeficiency Virus (FIV) pada Kucing

Abstract

The health of a cat is an aspect and a major concern for someone who maintains it. Cats can be exposed to many diseases, one of which is Feline Immunodeficiency Virus. Feline Immunodeficiency Virus, also known as feline aids, is a disease that attacks the cat's immune system. This virus causes a decrease in the cat's immune system so that cats become vulnerable and susceptible to disease. This disease is difficult to detect because the symptoms are almost invisible. This study aims to explore new knowledge stored in the data list of cats suffering from FIV so that the cause of the disease can be known and how to solve it. The data used in this study were 50 data obtained from the Animal Hospital of West Sumatra with attributes of Body Weight (BB), Body Temperature (ST), Breathing (PN), Appetite (NM), Vomiting (MN), Dental Condition (KG), Feather Condition (KB), Skin Condition (KK) and yield. The data is processed using the C4.5 Algorithm method based on the symptoms experienced by the cat. After processing, the data will produce the entropy value and gain value for each symptom, then the right decision tree is made for cats with FIV disease. The results of this study are new knowledge about FIV disease in cats in the form of a decision tree from the symptoms they experience. The results show that there are 21 rules with the chosen determinant attribute, namely the Skin Condition (KK) attribute with a gain value of 0.398683. This research is useful for the community, especially those who keep cats so that they can be used as a reference in knowing the symptoms experienced by cats if there is an indication of FIV disease.