Expert System Identifying Peanut Plant Diseases Using a Web-Based Certainty Factor Method (Case Study: Hambapraing Village)
DOI:
https://doi.org/10.59934/jaiea.v5i1.1301Keywords:
Expert Systems, Certainty Factor, Plant Diseases, Peanuts, Web-BasedAbstract
Peanuts (Arachis hypogaea L.) are one of the important agricultural commodities in Indonesia, especially in rural areas such as Hambapraing Village, East Sumba Regency. The productivity of these plants often decreases due to disease attacks that are difficult to recognize early due to the similarity of symptoms and limited access to experts. This research aims to develop a web-based expert system using the Certainty Factor method to assist farmers in identifying peanut plant diseases independently. The Certainty Factor method is used to handle uncertainty in the diagnosis process by providing a level of confidence in the user's chosen symptoms. The system was tested through 15 test scenarios and was able to achieve an accuracy level of 80%. These results show that the developed system can be a practical solution in detecting and managing plant diseases more quickly and precisely, especially in areas with limited access to agricultural experts.
Downloads
References
H. Listiyono, "Designing and Creating an Expert System," J. Teknol. Inf. Din., vol. XIII, no. 2, pp. 115–124, 2008.
N. A. Litau et al., "Development of Expert Systems in Information Systems Management of non-expert systems in solving complex problems. Expertise in proper and accurate correction systems. Where, this system also utilizes process capabilities," vol. 1, no. 4, 2023.
A. W. Widianto, N. Hidayat, and M. C. Mahfud, "Expert System for Identifying Peanut Plant Diseases Using the Android-Based Fuzzy Mamdani Method," J. Pengemb. Technology. Inf. and Computing Science., vol. 2, no. 8, pp. 2840–2845, 2018.
N. Silvia Tri Cahyani, N. Hidayat, and E. Santoso, "Classification of Peanut Plant Diseases using the MKNN (Modified K-Nearest Neighbor) Method," J. Pengemb. Technology. Inf. and Computing Science., vol. 7, no. 3, pp. 1191–1197, 2023, [Online]. Available: http://j-ptiik.ub.ac.id
N. Rahmi Sagala and L. Adiva, "Identification of Leaf Rust Disease (Puccinia arachidis) in Peanuts (Arachis hypogea L.) Microscopically and macroscopically in the Pest Control and Biological Agents Laboratory of the City of Padang," SEMNAS Bio 2023Pp. 1138–1142, 2023.
N. M. Afini, F. Triutami, N. A. Karenina, and H. N. Malika, "Fungus Causes Leaf Spot Disease on Peanuts (Arachis hypogaea)," Pros. Semin. Nas. Biol., vol. 2, no. 2, pp. 72–81, 2022.
H. Idris, A. Agustien, and M. Mansyurdin, "Control of Athelia rolfsii Causes of Stem Base Rot in Arachis hypogea Peanuts. l with plant fungicides and biological agents (review)," J. AGROSCIENCE and Technology., vol. 8, no. 2, p. 87, 2023, doi: 10.24853/jat.8.2.87-93.
A. Bilfi Arzan, A. Nadia Ciptaningrum, A. Sakinah, M. Ishlah, T. Zanki Haidar, and L. Advinda, "Disease-Causing Bacteria In Peanut Plants (Arachis hypogaea) and The Control Methods," Pros. SEMNAS BIO 2022 UIN Syarif Hidayatullah Jakarta, vol. 2, no. Proceedings of SEMNAS BIOLOGY 4 2022, pp. 193–200, 2022.
S. Asnunun, K. P. Kartika, T. Informatika, and U. I. Balitar, "Website-Based Soybeans," J. Inform. PolinemaPp. 61–72, 2020.
L. K. Hamim, B. Imran, and A. Akbar, "Web-Based Expert System for Disease Diagnosis in Mung Bean Plants Using the Dempster Shafer Method," J. Comput. Technol., vol. 1, no. 1, pp. 41–49, 2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Artificial Intelligence and Engineering Applications (JAIEA)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.







