A Chatbot-Based Expert System for Food Crop Disease Diagnosis
Abstract
The agricultural sector is very important for Indonesia's food security, however, plant diseases pose a serious threat that can significantly reduce crop yields. The limited availability of agricultural experts often hinders farmers from obtaining rapid and accurate diagnoses. To address this issue, this research develops a chatbot-based expert system using the forward chaining method to assist farmers in conducting self-diagnosis. This method works by drawing conclusions from the symptoms entered, while the chatbot provides real-time interactions that are easy to use. System testing shows good performance, black box testing ensures that all features operate without errors, with a diagnostic accuracy of 87.5 percent, as 42 out of 48 cases correspond with expert assessments. Furthermore, usability testing with 52 respondents yields a System Usability Scale score of 79.18, categorized as good. The results of this research indicate that the developed system is accurate, efficient, and practical, with the potential to serve as a widely applicable solution to help
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