Implementation of Forward Chaining And Certainty Factor Methods for Android-Based Red Onion Diagnosis

Abstract

Shallots are one of the crucial horticultural commodities in Indonesia, used in various social layers. Brebes is one of the main shallot producing regions with a significant increase in production. However, farmers often experience reduced yields due to disease attacks and lack of guidance from experts. This researcher aims to develop an Android-based expert system that applies the Certainty Factor and Forward Chaining methods to identify diseases in shallot plants. This system uses rules to identify onion disease symptoms and calculates the confidence level for each possible diagnosis. The Forward Chaining method helps identify symptoms sequentially, while the Certainty Factor calculates confidence in the possibility of disease. The research results show that this method is effective in providing an accurate diagnosis of onion diseases from the 5 diseases tested by the recommended system with a percentage value of 100%. In conclusion, the expert system created for diagnosing shallot plants using the Android-based forward chaining and certainty factor method was successfully built. Then, for Functionality Testing based on black box testing carried out by experts, the results were obtained with 100% accuracy, which means the system is in accordance with its functional requirements.