Sistem Pakar Diagnosa Penyakit Busuk Kuncup Pada Tanaman Sawit Menggunakan Trend Moment - Siddik, Muhammad; Abdullah, Iqbal; Samsir, Samsir; Sirait, Azrai (LPPM Universitas Potensi Utama, 2025)
Bud rot disease in oil palm is one of the most serious threats that can significantly reduce productivity and even cause plant death if not detected early. To support a faster and more accurate diagnosis process, this study developed a web-based expert system that applies the Trend Moment method. The system is built on a knowledge base containing the main symptoms of the disease, including wilted and rotting young leaves (G001), foul odor from the bud (G002), easily detached young leaves due to decay (G003), and rotting crown with brown mucus (G004). The system is able to identify three types of diseases, namely bud rot, Phytophthora palmivora, and Erwinia spp.. The diagnosis process is carried out by calculating the weight of symptoms selected by the user and determining the most probable disease based on the highest Trend Moment value. Experimental results on 20 test cases showed that the system achieved an accuracy rate of 100% when compared with expert diagnoses. These findings indicate that the developed expert system has strong potential to be an effective tool for farmers and field extension workers in detecting and managing oil palm diseases at an early stage.