EXPERT SYSTEM DESIGN TO DIAGNOSE PESTS AND DISEASES ON LOCAL RED ONION PALU USING BAYESIAN METHOD

  • Junaidi Junaidi Statistics Study Program, Mathematics and Natural Sciences Faculty, Tadulako University, Indonesia
  • Fadjryani Fadjryani Statistics Study Program, Mathematics and Natural Sciences Faculty, Tadulako University, Indonesia
  • Iman Setiawan Statistics Study Program, Mathematics and Natural Sciences Faculty, Tadulako University, Indonesia
  • Mohammad Batara Statistics Study Program, Mathematics and Natural Sciences Faculty, Tadulako University, Indonesia
  • Syaiful Hendra Information Technology Department, Engineering Faculty, Tadulako University, Indonesia
  • Nurmasita Ismail Assesment Institute for Agricultural Technology (AIAT) Sulawesi Tengah, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss1pp0371-0382
Keywords: Local Red Onion Palu, Pests and Diseases, Bayesian Method, Expert System

Abstract

Bayesian is a method that can be used to overcome the uncertainty of a situation or data. The information obtained must be continuously updated so that it can foster trust as a result of the uncertainty of those conditions. In this study, the application of the Bayesian method to detect early symptoms of diseases on local red onion Palu plants based on the symptoms that appear will be carried out. Information about pests and diseases that attack local red onion Palu is needed to help farmers. As a result, they can deal with attacked diseases quickly and precisely. This is crucial conducted by considering that this plant is one of the mainstay commodities for farmers in Central Sulawesi Province whose production must continue to be increased. Pests and diseases can be diagnosed through visible symptoms.The sample is local red onion Palu that affected by pests and disesases which planted in the AIAT of Central Sulawesi by experiment. As a result, through these symptoms an expert system can then be created to do a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The created expert system to diagnose diseases uses the Bayesian method to calculate the probability of an event occurring based on the obtained results from observations and experts. An expert system for diagnosis of pests and diseases is built on a web-based basis. This expert system has features and functions including the diagnosis of pests and diseases of the observed plants, viewing the results of the diagnosis and printing the results of the diagnosis. In addition, users can view information on pests and other diseases that attack plants. From the results of system testing that conducted by experts, this shows that the expert system is feasible to use to diagnose local red onion Palu plants which affected by pests and diseases with an accuracy point that has the largest percentage value.

Downloads

Download data is not yet available.

References

F. Bullard, “A Brief Introduction to Bayesian Statistics,” The North Carolina School of Science and Mathematics, vol. 60, no. 1, 2001.

V. Rossi, T. Caffi, and F. Salinari, “Helping farmers face the increasing complexity of decision-making for crop protection,” Phytopathologia Mediterranea, vol. 51, no. 3. 2012.

Chitra et al., “POTENSI PENGEMBANGAN USAHA PENGOLAHAN BAWANG GORENG LOKAL DI KOTA PALU Potency of Developing Local Fried Shallot Processing Business in Palu,” 2017.

Istriningsih et al., “Farmers’ knowledge and practice regarding good agricultural practices (GAP) on safe pesticide usage in Indonesia,” Heliyon, vol. 8, no. 1, Jan. 2022, doi: 10.1016/j.heliyon.2021.e08708.

I. D. Rafi Syahputra, Agung Triayudi, “Application Of Expert System To Diagnose Pests And Diseases In Coffee Plant Using Web-Based Naïve Bayes,” Jurnal Mantik Volume 3 Number 4, February 2020, pp. 383-392 E-ISSN 2685-4236, vol. 3, no. 4, 2020.

J. Liu and X. Wang, “Plant diseases and pests detection based on deep learning: a review,” Plant Methods, vol. 17, no. 1. 2021. doi: 10.1186/s13007-021-00722-9.

I. H. Santi and B. Andari, “Sistem Pakar Untuk Mengidentifikasi Jenis Kulit Wajah dengan Metode Certainty Factor,” INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi, vol. 3, no. 2, 2019, doi: 10.29407/intensif.v3i2.12792.

S. Sibagariang, “SISTEM PAKAR DIAGNOSA PENYAKIT SAPI DENGAN METODE CERTAINTY FACTOR BERBASIS ANDROID,” Jurnal TIMES, vol. 3, no. 2, 2008.

M. E. Maitland, “A transdisciplinary definition of diagnosis,” Journal of Allied Health, vol. 39, no. 4. 2010.

J. Adler, “Diagnosa Penyakit dengan Gejala Demam pada Manusia Berbasis Mobile : Knowledge Based System,” Komputika : Jurnal Sistem Komputer, vol. 6, no. 2, 2019, doi: 10.34010/komputika.v6i2.1607.

V. Purnama Dewa, A. Pujianto, and H. Putra, “SISTEM PAKAR DIAGNOSA PENYAKIT BUAH NANAS MENGGUNAKAN ALGORITMA BAYES BERBASIS WEB,” 2017.

T. Cabai et al., “RANCANG BANGUN SISTEM PAKAR UNTUK MENDIAGNOSIS,” Jurnal Rekayasa Dan Manajemen Sistem Informasi, vol. 2, no. 2, 2016.

W. C. WAHYUN and A. S. SITIO, “Pest Detection Expert System And Method Using Bayes Rice Diseases,” Journal Of Computer Networks, Architecture and High Performance Computing, vol. 2, no. 2, 2020, doi: 10.47709/cnapc.v2i2.411.

E. Stojanovski, D. Nur, E. Stojanovski, and D. Nur, “Prior Sensitivity Analysis for a Hierarchical model,” Proceedings of the-4th Annual ASEARC Conference Paramatta, Australia, 2011.

B. M. Penelitian, E. Revisi, and U. Riau, “Metodologi Penelitian,” 2021. [Online]. Available: https://www.researchgate.net/publication/354697863

Published
2023-04-16
How to Cite
[1]
J. Junaidi, F. Fadjryani, I. Setiawan, M. Batara, S. Hendra, and N. Ismail, “EXPERT SYSTEM DESIGN TO DIAGNOSE PESTS AND DISEASES ON LOCAL RED ONION PALU USING BAYESIAN METHOD”, BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0371-0382, Apr. 2023.
Issue
Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Junaidi Junaidi, Fadjryani Fadjryani, Iman Setiawan, Mohammad Batara, Syaiful Hendra, Nurmasita Ismail

Creative Commons License

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

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.