Cocoa Plant Disease Detection Using Expert System in Polewali Mandar
Sistem Pakar untuk Mendeteksi Penyakit pada Tanaman Kakao di Kantor Balai Penyuluhan Pertanian Polewali Mandar
Keywords:
artificial intelligence, expert system, cacao plant, disease diagnosis, backward chainingAbstract
Expert systems are computer-based programs designed to emulate human expertise in solving specific domain problems through the use of a structured knowledge base and logical inference mechanisms. This study aims to develop an expert system capable of diagnosing diseases in cacao plants by applying an inference method based on symptom analysis. The system was developed using a Research and Development (R&D) approach, encompassing several stages including requirement analysis, system design, development, validation, and field testing. The system design utilized the Unified Modeling Language (UML) to visually represent functional processes through use case, activity, and sequence diagrams. The inference process applied the backward chaining method to trace potential disease hypotheses based on the observed symptoms. Data were collected through field observations and expert interviews to build the knowledge base. System implementation was carried out through an interactive web-based interface designed to accommodate different user levels. Functional evaluation using the Black Box testing method demonstrated that all core system components, including data input, knowledge management, and disease diagnosis, operated as expected. The developed expert system effectively assists users in identifying cacao plant diseases accurately and efficiently. Overall, the research contributes to enhancing digital-based agricultural decision support systems and provides a practical model for applying artificial intelligence in tropical agricultural disease management.
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