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All Journal The Indonesian Journal of Computer Science
Panggih Suseno
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Diagnosis Penyakit Tanaman Kopi Robusta Menggunakan Metode Dempster Shafer Berbasis Sistem Pakar Acihmah Sidauruk; Panggih Suseno; Budy Satria; Mulia Sulistiyono
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3953

Abstract

Robusta coffee is a coffee variety that has unique characteristics, a strong taste and a different level of bitterness from Arabica coffee because Robusta coffee contains lower sugar and 2.2% more caffeine than Arabica coffee so that Robusta coffee production is quite helpful for the economy. several coffee producing countries in the world. The quality and productivity of coffee plants can decrease due to several factors such as pests and disease. However, the limitations of experts regarding coffee plant diseases are a factor and obstacle. The aim of this research is to create an expert-based intelligent system to identify pests and diseases in robusta coffee plants. The method that will be applied is Dempster Shafer. Data on disease names amounted to 13 and data on symptoms amounted to 27. The final result was that Robusta coffee plants were tested for expertise on the system with an average accuracy of diagnosis results of 94% from 13 test cases on pests and diseases of coffee plants, so it can be concluded that the system Experts can diagnose coffee plant pests and diseases very well using the Dempster Shafer method
Co-Authors Acihmah Sidauruk Mulia Sulistiyono