DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSING EGGPLANT DISEASES USING THE TSUKAMOTO FUZZY LOGIC METHOD - Efrizal Yudhantoro; Yoannes Romando Sipayung (Universitas Serang Raya, 2026)
Eggplant diseases are a major factor contributing to decreased crop quality and yield, particularly among novice farmers with limited knowledge of early disease identification. The uncertainty of symptom manifestation and limited access to agricultural experts further increase the risk of crop failure. This study aims to develop a web-based expert system for diagnosing eggplant diseases using the Tsukamoto Fuzzy Logic method. The novelty of this research lies in the integration of weighted symptom severity, fuzzy inference rules, and confidence-level outputs into a practical decision-support system specifically designed for eggplant disease diagnosis. The research adopts the Waterfall development model, including requirements analysis, system design, implementation, and testing. The knowledge base consists of five main diseases and twenty symptoms with weighted values ranging from 0.55 to 1.00. System evaluation using Black Box Testing shows that 100% of functional features operate successfully according to system requirements. Furthermore, diagnostic results demonstrate high confidence levels, reaching up to 97% for certain disease cases, indicating reliable system performance in handling uncertainty. This study contributes to the development of intelligent agricultural decision-support systems by providing an accessible, accurate, and efficient diagnostic tool. The proposed system can assist farmers in early disease detection, reduce dependency on experts, and potentially minimize crop losses while improving eggplant productivity. Keywords: Expert System, Eggplant Disease, Tsukamoto Fuzzy Logic, Decision Support System, Smart Agriculture