Expert System for Diagnosing Red Bean Plant Disease using Naïve Bayes Method

  • Ngivanul Mu'iz Program Studi Teknik Informatika, Universitas Sains Al-Qur’an
  • Hidayatus Sibyan Program Studi Teknik Informatika, Universitas Sains Al-Qur’an
  • Rina Mahmudati Program Studi Teknik Informatika, Universitas Sains Al-Qur’an
DOI: https://doi.org/10.32699/cathasaintifica.v3i1.8824
Keywords: Expert System, Disease Diagnosis, Red Bean Plants, Naive Bayes

Abstract

Innovation in the development of various kinds of technology in various fields and fast sources of information, the use of these developments is used to overcome various challenges that arise, including in the field of agriculture. The main factor of problems that are often faced by farmers is in identifying diseases such as in plants including kidney bean plants, the main cause of the problem is the lack of knowledge about the disease, the absence of an expert in the field, and there is no expert system for diagnosing kidney bean plant diseases. The naïve bayes method is a method of calculation to determine a possibility, The implementation of the naïve bayes method in this expert system is carried out to determine the diagnosis of kidney bean disease based on kidney bean information, such as symptom data and emerging disease data. Determining the diagnosis of a disease from the calculation of the naïve Bayes method is based on the highest probability of disease diagnosis. Building an expert system for diagnosing diseases of red beans based on websites is used to assist farmers in diagnosing diseases and finding solutions based on the symptoms that arise.  The results of this expert system test, taken from the system test on experts, obtained an accuracy score of 90%. This research produces efficient and easily accessible digital tools, which play a role in supporting food security and increasing farmers' productivity through a faster and more accurate disease diagnosis process.

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References

Ananta, K. Y., Hartanti, D., & Sundari, S. (2025). Expert System for Diagnosing Chili Plant Diseases Using the Naive Bayes Method. Smart Comp: Jurnalnya Orang Pintar Komputer, 14(1), 21–31.
Arianto, F. S. D., & Firgia, L. (2022). Theory and Application of ARTIFICIAL INTELLIGENCE Case Study Using Web Programming. Uwais Inspirasi Indonesia.
Badrul, M. (2021). Implementation of the Waterfall Method for Designing an Inventory Information System at the Bintang Terang Ceramics Store. PROSISKO: Jurnal Pengembangan Riset Dan Observasi Sistem Komputer, 8(2), 52–57.
Dewi, I. C., Soebroto, A. A., & Furqon, M. T. (2015). Expert system for diagnosing beef cattle diseases using the naive bayes method. Journal of Environmental Engineering and Sustainable Technology, 2(2), 72–78.
Djollong, A. F. (2014). Quantitative research implementation techniques. Istiqra: Jurnal Pendidikan Dan Pemikiran Islam, 2(1).
Dwiramadhan, F., Wahyuddin, M. I., & Hidayatullah, D. (2022). Expert System for Diagnosing Cat Skin Diseases Using Web-Based Naive Bayes Method. J. JTIK (Jurnal Teknol. Inf. Dan Komunikasi), 6(3), 429–437.
Ernawati, D., Yusda, R. A., & Putra, G. M. (2021). Disease Analysis in Chili Plants Using Web-Based Case Based Reasoning Method. J-Com (Journal of Computer), 1(1), 43–48.
Firdaus, T., & Yanti, F. (2022). Implementation of the Naïve Bayes Method in a Web-Based Dental Disease Diagnosis Expert System.
Hamim, L., Imran, B., & Akbar, A. (2023). Web-Based Expert System for Diagnosing Green Bean Plant Diseases Using the Dempster Shafer Method. Journal Computer and Technology, 1(1), 41–49.
Handoko, M. R., & Neneng, N. (2021). Expert System for Diagnosing Diseases During Pregnancy Using Web-Based Naive Bayes Method. Jurnal Teknologi Dan Sistem Informasi, 2(1), 50–58.
Imandasari, T., Irawan, E., Windarto, A. P., & Wanto, A. (2019). Naive Bayes Algorithm in Classification of Water Source Development Locations. Pros. Semin. Nas. Ris. Inf. Sci, 1, 750.
Kurniawan, W., Muriyatmoko, D., & Izzati, F. Z. (2024). Application of Naïve Bayes Method in Expert System for Predicting Sugarcane Plant Diseases. Prosiding Seminar Nasional Penelitian LPPM UMJ.
Putra, D. W. T., & Andriani, R. (2019). Unified Modeling Language (UML) in designing information systems for SPPD restitution payment applications. Jurnal Teknoif Teknik Informatika Institut Teknologi Padang, 7(1), 32–39.
Salmi, N., & Rustam, Z. (2019). Naïve Bayes classifier models for predicting the colon cancer. IOP Conference Series: Materials Science and Engineering, 546(5), 052068.
Suherman, B. B. (2021). Expert System for Diagnosing Diseases and Pests in Corn Plants Using the Naive Bayes Method. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 2(3), 390–398.
Syarifudin, A., Hidayat, N., & Fanani, L. (2018). Expert System for Diagnosing Corn Plant Diseases Using the Android-Based Naive Bayes Method. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(7), 2738–2744.
Published
2025-06-09
How to Cite
Mu’izN., SibyanH., & MahmudatiR. (2025, June 9). Expert System for Diagnosing Red Bean Plant Disease using Naïve Bayes Method. CATHA SAINTIFICA, 3(1), 10-19. https://doi.org/https://doi.org/10.32699/cathasaintifica.v3i1.8824
Issue
Vol 3 No 1 (2025): May 2025 Article-in-Press
Section
Article
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