SISTEM CERDAS BERBASIS MACHINE LEARNING UNTUK DIAGNOSIS PENYAKIT PADA KUCING

Abstract

The challenge of diagnosing cat diseases quickly and accurately, caused by the tendency of cats to hide pain and often non-specific clinical symptoms, forms the primary background of this research. This study aims to design and build an intelligent system based on machine learning that can provide initial diagnostic recommendations for common cat diseases based on symptom data. The research method used adopts the Agile Scrum framework , with the K-Nearest Neighbor (KNN) algorithm as the classification core. System testing was conducted on 200 test data points covering five main diseases: Cat Flu, Worms, Fungal Infection, Rabies, and Diarrhea. The test results showed excellent performance with an average accuracy rate of 92.50%. Specifically, the system successfully classified 185 data points correctly and 15 incorrectly , with Rabies recording the highest accuracy (96.67%). Although there is still an error rate of 7.5% , this system is proven feasible for use as an initial diagnostic aid; however, its use must still be supported by direct confirmation from a professional veterinarian.