Expert System and IoT for Diagnose of Feline Panleukopenia Virus using Certainty Factor

Abstract

Cats are animals that are loved by many people and are widely used as pets. All things related to cat health will be pursued by cat owners. However, sometimes the prevention efforts that have been made by cat owners cannot stop the spread of cat diseases, especially those caused by viruses. One of the viruses that can infect cats is feline panleukopenia virus. Where this virus can be deadly and can spread easily. Sometimes the symptoms caused are like ordinary diseases and can not be easily understood by cat owners. Early diagnosis is needed to prevent this disease. What can be done is to create an expert system, which with this system can diagnose feline panleukopenia based on the initial symptoms seen. In addition, to support diagnosis, use IoT devices to determine the body temperature and heart rate of the cat. The purpose of this study is to provide an early prediction of Panleu disease in cats, so that it can make it easier for users to immediately follow up from the initial diagnosis obtained. The research was conducted by conducting a literature study, collecting and analyzing data, making designs and tools, implementing, and testing. The results obtained from this study used 13 samples obtained from veterinarians, where the results of the expert diagnosis were eight samples of acute panleukopenia, four samples of chronic panleukopenia, and 1 sample of non-panleukopenia. The results were obtained with an accuracy of 92 %. The average deviation value of the pulse sensor is 2.40 % and the average deviation value of the LM35 sensor is 1.30 %.