CASE BASED REASONING DIAGNOSIS PENYAKIT KULIT PADA KUCING DENGAN METODE SIMILARITAS EUCLIDEAN DISTANCE DAN METODE SIMILARITAS NEAREST NEIGHBOR

Abstract

Cat health is also very important like human health. Especially in the type of disease that is quite serious. The problem that often arises is that most cat owners who do not know the symptoms and illnesses suffered by cats and do not immediately check with a veterinarian on the grounds that the costs incurred are not cheap. Therefore, to reduce the problem above, the system was built using Case Based Reasoning with Euclidean Distance Similarity method and Nearest Neighbor Similarity method by utilizing the knowledge of old cases to new cases by finding the similarity value of each case so as to produce a maximum level of accuracy. . The data contained in this system there are 3 types of disease data, 10 data symptoms and 3 data solutions obtained through a direct interview process with a veterinarian. Case data (source case) from this research are 50 data and for test data that is 50 data. The system accuracy of this study is 96% with a threshold of 0.1-0.6 for the Euclidean Distance Similarity method and the accuracy of the system with the Nearest Neighbor Similarity method of 96% with a threshold of 0.1-0.4.