Application Of Expert System In Determining Diseases In Potato Plants

This research aims to develop an expert system in diagnosing diseases in potato plants using the Case Based Reasoning (CBR) method approach combined with the K-Nearest Neighbor (K-NN) algorithm. The system is designed to help farmers identify the type of disease based on the symptoms that appear, as well as provide relevant solutions to increase crop productivity. In previous research, the CBR method showed a limited accuracy rate of 74% because it only relied on one algorithm. Through the application of two methods in data analysis, namely CBR and K-NN, this study succeeded in increasing the diagnosis accuracy to be higher than the previous approach of 80%. The system is implemented in the form of a web-based application that is easily accessible by farmers. The results show that the integration of these two methods provides more optimal, effective, and accurate results in detecting potato plant diseases based on symptom data. The findings are expected to contribute significantly to the development of agricultural technology, especially in improving the harvest success of potato farmers in Indonesia.