Application of the Case-Based Reasoning (CBR) Method in the Web-Based Rice Plant Disease Diagnosis Expert System

Abstract

The agricultural sector is the main pillar of the Indonesian economy with rice as a strategic commodity for food security. Rice production faces serious challenges, such as climate change and bacterial blight and blast disease attacks that have reduced productivity by up to 40% in Pahunga Lodu District, East Sumba. Limited experts and access to information hinder effective disease management. This research develops a web-based expert system using the Case-Based Reasoning (CBR) method to help farmers diagnose rice diseases quickly and accurately. The development of the system used the Waterfall model, with data obtained through observation, interviews, and documentation. The implementation results show that all key features such as login, symptom input, disease data management, and diagnostic process work well through black-box testing. In addition, through a 10-case test scenario, the system is able to provide diagnostic results with an accuracy rate of 80%. This proves that the system can be an effective solution in supporting the management of rice plant diseases. It is hoped that this system can increase the independence and productivity of farmers and support food security in the region