Expert System for Diagnosing Diseases in Coffee Plants Using Forward Chaining and Classic Probability Algorithms Case Study: West Lampung

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Published Dec 3, 2024
DOI https://doi.org/10.20895/inista.v7i1.1633

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Eka Revi Novita
Information System, Raden Intan State Islamic University of Lampung
Indra Gunawan
Information System, Raden Intan State Islamic University of Lampung
Wawan Gunawan
Information System, Raden Intan State Islamic University of Lampung

Abstract

Coffee is a highly demanded product and a key commodity in Indonesia due to its significant market value and contribution to the country's foreign exchange. The majority of coffee plantations are found in highland areas, particularly in West Lampung Regency, which produced approximately 56,054 tons of coffee in 2022. However, coffee production has been declining due to low productivity and quality. One major issue is the limited knowledge of coffee farmers in West Lampung about diseases affecting coffee plants, leading to improper treatment. Another challenge is the lack of experts to guide farmers in managing and treating coffee diseases effectively. To address these problems, an expert system is needed to provide information and solutions for dealing with coffee diseases. This system incorporates expert knowledge into a computerized platform to assist in diagnosing coffee plant diseases. The system is developed using the waterfall method and employs forward chaining and classic probability algorithms to diagnose diseases and calculate the accuracy of results. Users can diagnose based on symptoms and receive treatment recommendations. This web-based expert system aims to assist farmers in early disease diagnosis and provide appropriate solutions for managing coffee diseases

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How to Cite
Novita, E., Gunawan, I., & Gunawan, W. (2024). Expert System for Diagnosing Diseases in Coffee Plants Using Forward Chaining and Classic Probability Algorithms. Journal of Informatics Information System Software Engineering and Applications (INISTA), 7(1), 33-50. https://doi.org/10.20895/inista.v7i1.1633
Issue
Vol 7 No 1 (2024): November 2024
Section
Articles
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