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fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>http://repository.teknokrat.ac.id/2371/</relation><title>APPLICATION OF DISEASE DETECTION IN PEPPERPLANT USING RULE BASED REASONING METHOD</title><creator>Pertiwi, Dian</creator><subject>PERANCANGAN SISTEM INFORMASI</subject><subject>PERANCANGAN APLIKASI</subject><subject>TJ Mechanical engineering and machinery</subject><description>Disease on paper plants is undesirable for people who have paper planting because it can&#xD; make the harvest are not maximum and finally paper plants will die. To solve this&#xD; problem, people need knowledge about the information of the disease, symptom, and how&#xD; to handling the disease. Based on this problem, we need expert system to diagnose the&#xD; disease on paper plants. This system aims to plan and built Web and Mobile expert system.&#xD; In this research, expert system is built based on Web and Mobile system and java&#xD; programming language as the database. The reasoning method Rule based Reasoning used&#xD; in this research is forward chaining. This method is used to determine which rules will be&#xD; used, then it is executed, finally the process is repeated until the results are found. This&#xD; system could diagnose 3 diseases of pepper plants with 1 symptoms. From the data&#xD; result, using Equivalence Partitioning showed that the manager rule system could&#xD; run according to function and system could diagnose the disease well. In addition,&#xD; based on questionnaire data, this application was user friendly application (with the&#xD; average value 91% or very good) from 40 respondents.</description><date>2019-11-12</date><type>Thesis:Thesis</type><type>PeerReview:NonPeerReviewed</type><type>Book:Book</type><language>eng</language><identifier>http://repository.teknokrat.ac.id/2371/1/14.%20Abstract.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.teknokrat.ac.id/2371/2/15.%20BAB%20I%20Pendahuluan.pdf</identifier><type>Book:Book</type><language>eng</language><identifier>http://repository.teknokrat.ac.id/2371/3/20.Daftar%20Pustaka.pdf</identifier><identifier> Pertiwi, Dian (2019) APPLICATION OF DISEASE DETECTION IN PEPPERPLANT USING RULE BASED REASONING METHOD. Other thesis, Universitas Teknokrat Indonesia. </identifier><recordID>2371</recordID></dc>
language eng
format Thesis:Thesis
Thesis
PeerReview:NonPeerReviewed
PeerReview
Book:Book
Book
Thesis:Bachelors
author Pertiwi, Dian
title APPLICATION OF DISEASE DETECTION IN PEPPERPLANT USING RULE BASED REASONING METHOD
publishDate 2019
topic PERANCANGAN SISTEM INFORMASI
PERANCANGAN APLIKASI
TJ Mechanical engineering and machinery
url http://repository.teknokrat.ac.id/2371/1/14.%20Abstract.pdf
http://repository.teknokrat.ac.id/2371/2/15.%20BAB%20I%20Pendahuluan.pdf
http://repository.teknokrat.ac.id/2371/3/20.Daftar%20Pustaka.pdf
http://repository.teknokrat.ac.id/2371/
contents Disease on paper plants is undesirable for people who have paper planting because it can make the harvest are not maximum and finally paper plants will die. To solve this problem, people need knowledge about the information of the disease, symptom, and how to handling the disease. Based on this problem, we need expert system to diagnose the disease on paper plants. This system aims to plan and built Web and Mobile expert system. In this research, expert system is built based on Web and Mobile system and java programming language as the database. The reasoning method Rule based Reasoning used in this research is forward chaining. This method is used to determine which rules will be used, then it is executed, finally the process is repeated until the results are found. This system could diagnose 3 diseases of pepper plants with 1 symptoms. From the data result, using Equivalence Partitioning showed that the manager rule system could run according to function and system could diagnose the disease well. In addition, based on questionnaire data, this application was user friendly application (with the average value 91% or very good) from 40 respondents.
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