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Roznim, Roznim
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparison of Case-Based Reasoning and Hybrid Case-Based Methods in Expert System for Diagnosing Rice Plant Diseases Roznim, Roznim; Mesran, M.Kom, Mesran; Setiawansyah, Setiawansyah; Ambarsari, Erlin Windia
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.132

Abstract

Rice plants are susceptible to various types of diseases that can reduce productivity and quality of the harvest. Therefore, an expert system is needed that can help the disease diagnosis process quickly and accurately. This study compares two approaches in expert systems, namely the Case-Based Reasoning (CBR) method and the Hybrid Case-Based method, to diagnose rice plant diseases based on the symptoms experienced. Data on symptoms and types of diseases were analyzed using both methods to see the level of suitability of the resulting diagnosis. The test results showed that the Hybrid Case-Based method produced a higher level of certainty for all types of diseases compared to the CBR method. For example, Bacterial Leaf Blight disease has a certainty value of 99.5% in the Hybrid method, higher than 83.8% in the CBR method. These findings indicate that the Hybrid method is more effective and accurate in the process of diagnosing rice plant diseases. Thus, an expert system based on the Hybrid Case-Based method is recommended to support decision making in the agricultural sector, especially in early detection of rice diseases
Co-Authors Erlin Windia Ambarsari Mesran, Mesran Setiawansyah Setiawansyah