Implementasi Certainty Factor Dalam Sistem Pakar Untuk Akurasi Diagnosa Penyakit Ikan Lele
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Diseases in catfish are a major cause of reduced productivity in aquaculture and economic losses for fish farmers. The process of disease identification is often suboptimal due to the farmers' limited knowledge in recognizing symptoms, which risks delayed treatment and increased fish mortality. This study aims to develop a web-based expert system as an aid for the early identification of diseases in catfish by implementing the Certainty Factor (CF) method. The system is designed to facilitate quicker, systematic, and measurable diagnosis. The advantage of this research lies in the integration of expert knowledge with the CF method, producing diagnoses accompanied by confidence levels to support more objective decision-making. The system development follows the Waterfall model, encompassing needs analysis, design, implementation, and testing. The knowledge base includes seven types of catfish diseases and their symptoms. System testing was conducted through diagnostic scenarios, comparing the system's results with expert diagnoses. Results indicate that the system can identify diseases and calculate the confidence level of diagnoses. The disease Enteric Septicemia of Catfish (ESC) received the highest CF value of 0.982 (98.2%), while Motile Aeromonad Septicemia (MAS) scored 0.745 (74.5%). This study contributes a web-based diagnostic system that supports quick and structured identification, aiding farmers in timely disease management.
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