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All Journal Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Novita, Eka Revi
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Computer Science & IT

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Expert System for Diagnosing Diseases in Coffee Plants Using Forward Chaining and Classic Probability Algorithms Novita, Eka Revi; Gunawan, Indra; Gunawan, Wawan
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1633

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
Co-Authors Indra Gunawan Wawan Gunawan