Expert System to Diagnose Diseases in Durian Plants using Naïve Bayes


  • Narantyo Maulana Adhi Nugraha Institut Teknologi Telkom Purwokerto, Jawa Tengah, Indonesia
  • Reva Rahardian Institut Teknologi Telkom Purwokerto, Jawa Tengah, Indonesia
  • Adam Nur Kridabayu Institut Teknologi Telkom Purwokerto, Jawa Tengah, Indonesia
  • Faisal Dharma Adhinata * Mail Institut Teknologi Telkom Purwokerto, Jawa Tengah, Indonesia
  • Nur Ghaniaviyanto Ramadhan Institut Teknologi Telkom Purwokerto, Jawa Tengah, Indonesia
  • (*) Corresponding Author
Keywords: Disease; Durian; Expert system; Farmer; Naive Bayes

Abstract

Durian is a fruit that is very popular and very easy to find throughout Indonesia. Durian fruit is a thorny fruit with a very pungent smell with a distinctive taste, and for some durian fans, the distinctive taste of durian is what makes durian unique compared to other fruits. However, it is unfortunate that the production and quality of durian fruit in Indonesia is currently still low due to the limited knowledge of farmers in caring for and maintaining durian plants from pests and diseases on durian plants. So far, in detecting pests and diseases, farmers still carry out pest and disease detection manually, and of course, this is very dependent on pest and disease observers/experts. For this reason, so that later the level of production and quality of durian in Indonesia can increase, we create an expert system to diagnose a disease in durian plants to help farmers overcome problems around pests and diseases commonly occur in durian plants. This study uses the Naïve Bayes method as a determinant of durian disease. The experimental results yield an accuracy of 82%, which indicates the proposed method is quite good in diagnosing durian disease.

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Article History
Submitted: 2021-12-20
Published: 2021-12-31
Abstract View: 2 times
PDF Download: 7 times
How to Cite
Nugraha, N. M. A., Rahardian, R., Kridabayu, A. N., Adhinata, F. D., & Ramadhan, N. G. (2021). Expert System to Diagnose Diseases in Durian Plants using Naïve Bayes. Building of Informatics, Technology and Science (BITS), 3(3), 346-352. https://doi.org/10.47065/bits.v3i3.1077
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