Coffee Plant Disease Identification System Using Factor Certainty Method Nita Wahyu Rini(1), Endah Ratna Arumi(2), Mukhtar Hanafi(3)
123) Informatics Engineering Study Program, Faculty of Engineering, Muhammadiyah University of Magelang, Magelang, Indonesia
Abstract
Coffee plants, vital in agriculture and globally cultivated, especially in Indonesia, a leading producer. Farmers struggle to promptly identify and treat diseases, causing substantial losses. To improve efficiency, a proposed system uses the certainty factor method for disease identification. Implementation of an identification system using the certainty factor method to speed up the provision of disease information and solutions to coffee plant diseases based on predetermined criteria and weights. This study employs the forward chaining method for planning, monitoring, and future-oriented reasoning. Additionally, the certainty factor method ensures accurate results by calculating user-experienced outcomes based on symptom confidence levels. The successful implementation of the identification system was confirmed through black box testing. Alpha black-box testing shows the system is functioning properly. Black-box Beta testing, involving 10 users, resulted in a success rate of 87%, indicating user satisfaction and system efficacy. The findings of this research have valuable implications for the agricultural sector, especially in coffee cultivation areas. The developed identification system can provide benefits to farmers, assisting in the timely detection and treatment of coffee plant diseases, thereby ultimately increasing crop yields and minimizing losses.
