Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi
Hermi Rani
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sistem Pakar Diagnosa Penyakit Tanaman Padi Menggunakan Metode Forward Chaining Ismail Ismail; Hermi Rani; Nabila Amelia
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.1752

Abstract

Rice is one of the leading commodities in Indonesia that has a strategic role in maintaining national food security. One of the main factors that hinders the increase in rice crop production is disease attacks, which can be caused by pathogens, host plant conditions, or less supportive environmental factors. The process of diagnosing rice crop diseases generally requires special expertise, knowledge, and experience from experts in the field of agriculture, the availability of which is still limited in some areas. Therefore, technology-based solutions are needed to assist farmers in making quick and accurate diagnoses. This research aims to build a mobile-based expert system that is able to diagnose 13 types of rice plant diseases based on 43 symptoms, by referring to the knowledge of three experts. The reasoning method used is forward chaining, while the uncertainty calculation method uses Shafer's Dempster theory. The results of the black box test showed that the expert system had a functional suitability rate of 100% based on all test scenarios carried out. In addition, the results of the theoretical calculation test showed that the system calculation was in accordance with the results of manual calculations. The accuracy test of the system on 30 test cases obtained an accuracy rate of 81.11%, so the system is considered quite reliable as a tool to diagnose rice plant diseases.
Co-Authors Ismail Ismail Nabila Amelia