Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/3515
Title: Turnitin Rubber Plant Disease Diagnostic System Using Technique for Order Preference by Similarity to Ideal Solution
Authors: Ramadiani, Ramadiani
Ramadhani, M Syahrir
Azainil, Azainil
Jundillah, Muhammad Labib
Keywords: Rubber Plant Disease; East Kalimantan; TOPSIS; Expert System
Issue Date: 2-Jan-2020
Publisher: Procedia Computer Science
Citation: Ramadiania, M. Syahrir Ramadhania, Muhammad Labib Jundillahb, Azainil. 2019. Rubber Plant Disease Diagnostic System Using Technique for Order Preference by Similarity to Ideal Solution. Procedia Computer Science 161 (2019) 484–492
Series/Report no.: https://doi.org/10.1016/j.procs.2019.11.148;
Abstract: Abstract Data from the Plantation Office of East Kalimantan Province, the area of ​​rubber plantations has decreased every year. One of the factors that make productivity of rubber plants low is the presence of pests and diseases. The time limit of an expert is an obstacle in identifying rubber plantations. To overcome this problem, we need an expert system that can identify rubber plant diseases such as an expert. This system was developed to be able to provide solutions in diagnosing rubber plant diseases such as White Root Fungus (Rigidoporus micropus), Upas Mushroom Disease (Corticium salmonicolor), Antraknosa Disease (Colletorichum gloeosporoides), Skin Necrosis (Fusarium sp.), And Cancer Lines (Phytoptora palmivore). This study uses the TOPSIS method. There are four variables used as assessment criteria in this study, namely: the level of damage to the root, the level of damage to the stem, the level of damage to the leaves and the level of damage to the tapping path or the intensity of tapping, with preference values ​​from 0 to 100; none, Very Light Intensity (1), Mild Intensity (3), Medium Intensity (5), Weight Intensity (7) and Very Heavy Intensity (9). Each weight value C1= 0.3; C2= 0.22; C3= 0.28; C4= 0.2. The results of this study compare manual calculations and calculations on the system obtained 99.99% accuracy. This system is expected to help facilitate rubber farmers in identifying diseases and can help experts.
URI: https://www.sciencedirect.com/science/article/pii/S1877050919318599
http://repository.unmul.ac.id/handle/123456789/3515
Appears in Collections:A - Computer Sciences and Information Technology



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.