Expert System Diagnosa Penyakit Pada Tanaman Kelapa Sawit Dengan Menggunakan Metode Forward Chaining

Setiawan, Yogi (2026) Expert System Diagnosa Penyakit Pada Tanaman Kelapa Sawit Dengan Menggunakan Metode Forward Chaining. Skripsi thesis, UIN Imam Bonjol Padang.

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Abstract

Oil palm (Elaeis guineensis Jacq.) is a strategic plantation commodity that plays an important role in Indonesia’s economy as it produces crude palm oil (CPO), which is widely used in the food, cosmetics, and renewable energy industries. However, oil palm productivity is often threatened by pest and disease attacks that can reduce both the quality and quantity of yields. Farmers frequently face difficulties in identifying disease types and determining appropriate control methods, which often leads to a trial-and-error approach that increases operational costs and results in excessive pesticide use. To address this problem, this study develops a web-based expert system using the forward chaining inference method to diagnose oil palm diseases. The system’s knowledge base consists of 48 symptoms and 15 types of diseases commonly found in oil palm plantations. The system was developed using the Waterfall software development model, which includes the stages of requirement analysis, design, implementation, testing, and maintenance to ensure a structured process. Furthermore, the system was evaluated using Black Box Testing to verify the functionality of each feature and ensure the system operated according to specifications, and the System Usability Scale (SUS) to measure usability and user satisfaction. The results indicate that the system can provide fast and accurate diagnoses along with appropriate control recommendations based on identified symptoms. Therefore, this expert system is expected to assist farmers and agricultural practitioners in detecting diseases more effectively, improving productivity, and supporting the development of technology-based sustainable agriculture in Indonesia.

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