Design and Implementaion of a Web-Based Expert System for Mango Fruit Disease Diagnosis Using the Naïve Bayes Method

Tri Prasetyo; Eka Sri Rahayu

doi:10.31572/inotera.Vol10.Iss2.2025.ID551

Tri Prasetyo Universitas Pamulang
Eka Sri Rahayu Universitas Pamulang

DOI: https://doi.org/10.31572/inotera.Vol10.Iss2.2025.ID551

Keywords: Naïve Bayes method, Agricultural commodity, Expert system, Disease diagnosis, Digital agriculture

Abstract

Mango is a leading agricultural commodity in the XYZ region with high economic value. However, its productivity and quality are often reduced due to various plant diseases. To assist farmers in addressing this issue, this study developed a web-based expert system utilizing the Naïve Bayes method to diagnose mango diseases based on identified symptoms. The system is designed not only to provide fast and accurate diagnoses but also to offer appropriate treatment recommendations. The research methodology includes data collection from various sources, system interface design, implementation of the Naïve Bayes algorithm, and system testing to evaluate diagnostic accuracy. The test results indicate that the system can deliver diagnoses with a high level of accuracy.The development of this expert system aims to empower farmers, especially in rural and under-resourced areas, by providing them with accessible technology to support agricultural decision-making. By digitizing the diagnostic process, farmers no longer need to rely solely on expert consultations, which may be time-consuming or unavailable in remote areas. This approach also promotes early detection and timely intervention, potentially reducing the spread of diseases and minimizing crop loss.In addition to its practical benefits, this research contributes to the field of agricultural informatics by demonstrating the application of probabilistic classification methods in real-world farming problems. Future enhancements may include integrating Internet of Things (IoT) technology for real-time plant monitoring and expanding the system to support a wider variety of crops and disease types. The integration of mobile platforms could also increase accessibility, allowing farmers to perform diagnoses directly from their smartphones in the field.

Downloads

Download data is not yet available.

References

M. T. Effendi, N. Hidayat, and R. K. Dewi, “Mango Plant Disease Diagnosis System Using the Naïve Bayes Method,” Journal of Information Technology and Computer Science Development, vol. 3, no. 4, pp. 3896–3902, 2019.

A. R. Auni and E. Sugiharti, “Optimization of Mango Leaf Disease Classification Using Concatenation of MobileNetV2 and DenseNet201 CNN Architectures,” Scientific Journal of Informatics, vol. 11, no. 4, pp. 1023–1034, 2025.

D. Faye, I. Diop, N. Mbaye, D. Dione, and M. M. Diedhiou, “Mango Fruit Disease Severity Estimation Based on Image Segmentation and Deep Learning,” Discover Applied Sciences, vol. 7, no. 2, pp. 0–14, 2025.

R. Y. Patil, S. Gulavani, V. B. Waghmare, and I. K. Mujawar, “Image-Based Detection of Anthracnose and Red-Rust Leaf Diseases Using Deep Learning,” Telkomnika: Telecommunication, Computing, Electronics, and Control, vol. 20, no. 6, pp. 1256–1263, 2022.

I. S. Dumayanty and S. Manurung, “Expert System for Detecting Chili Plant Diseases,” vol. 4, no. 2, pp. 10–20, 2024.

M. Arifin, G. A. Buntoro, and F. Masykur, “Cucumber Health Diagnosis Using Naïve Bayes with Web Integration,” Complet: Journal of Computers, Electronics, and Telecommunications, vol. 2, no. 02, pp. 1–6, 2021.

S. Surorejo, Y. P. Chaeriko, P. S. Ananda, P. Studi, and S. Informasi, “Application of the Naïve Bayes Method in an Expert System for Predicting Sugarcane Plant Diseases,” Proceedings of the National Seminar LPPM UMJ, vol. 3, no. 1, pp. 8–17, 2022.

J. Fathurahman, D. Hartanti, and S. Sopingi, “Sentiment Analysis of Joglo Wifi UMKM Service Using the Naïve Bayes Method,” Journal Inotera, vol. 9, no. 2, pp. 370–377, 2024.

W. Lestari, N. Budi Nugroho, W. Rista Maya, and F. Author, “Expert System for Diagnosing Diseases in Sechium edule Plants Using Bayes' Theorem Method,” Journal CyberTech, vol. 4, no. 6, pp. 39–45, 2021.

Sanusi, Rifky Hidayat, Ilham, Syahrul Hidayat, and Sarmila Putri, “Design of an Expert System for Disease Diagnosis in Chickens using the Certainty Factor Method”, JI, vol. 10, no. 1, pp. 55-61, Feb. 2025.

D. S. Putra, A. Rafli, I. M. Arinal, and R. Resky, “Expert System for Diagnosing Diseases in Children Aged One to Six Years Using the Forward Chaining Method”, JI, vol. 8, no. 2, pp. 352-358, Nov. 2023.