Implementasi Algoritme K-Means Clustering Dan Naive Bayes Classifier Untuk Klasifikasi Diagnosa Penyakit Pada Kucing - Puji Indah Lestari; Dian Eka Ratnawati; Lailil Muflikhah (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
At this time cat has become a popular pet community. This is because there are many benefits that exist from cats, such as an entertainer, and now developed countries many cats contested in the show cat. Treatment for cats is mainly because some of the cat's disease can spread to humans. The limitations of dentists in diagnosing diseases with a pattern of having the same symptoms as some diseases, are important in making a diagnosis. Therefore there need a system that can diagnose diseases that can be accessed by the cat owners and be dealt immediately. In this study can use K-Means Naive Bayes (KMNB) method for diagnosis in cats. The KMNB approach is formed by the incorporation of clustering and classification techniques. In the beginning Clustering on K-Means was used to group the same data. Further classification of data by category using Naive Bayes method. The data that have errors in the first stage are then organized by the second category. Identify data with the same character or data that shows similar characteristics from the start. Based on the results of tests that have been done by comparing the results of grouping on conventional K-Means proves that KMNB can produce the highest average of 90% while conventional K-Means has the highest average of 71, 379%.