Diseases in black tiger shrimp are crucial to monitor for their survival and productivity in aquaculture. Rapid detection through clinical symptoms, though not 100% accurate, is vital for prompt action. This study aims to diagnose diseases in black tiger shrimp using computer-based artificial intelligence based on observational input. A qualitative data analysis method is employed, with variables including clinical symptoms, eye damage, and body color change. Results show that body damage is the most influential factor in disease type. Although fuzzy logic is not 100% accurate, it is essential and beneficial for shrimp farmers.
                        
                        
                        
                        
                            
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