Designing an Expert System for Molly Fish Diseases Using Forward Chaining
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Abstract
Ornamental fish farming, especially Molly fish (Poecilia sphenops), is becoming increasingly popular due to its high aesthetic value and ease of care. However, the high mortality rate of fish due to disease outbreaks is a major obstacle for farmers, especially those who do not have sufficient knowledge about the symptoms and treatment of fish diseases. This study aims to design an expert system for diagnosing Molly fish diseases using the forward chaining method as a tool for early identification based on observed symptoms. The study was conducted using a descriptive qualitative approach that focused on non-numerical and interpretive exploration of the phenomenon, without conducting statistical testing or direct system implementation. The results of the study produced a conceptual design of an expert system that covers 25 types of diseases, 40 main symptoms, and 25 inference rules based on if-then logic as the basis for diagnostic reasoning. Conceptually, this system is expected to help farmers recognize Molly fish diseases more systematically and efficiently, as well as serve as the basis for the development of artificial intelligence technology applications in aquaculture and ornamental fisheries
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