Department of Informatics, Politeknik Harapan Bersama, Indonesia
BibTex Citation Data :
@article{JTSISKOM12901, author = {Ginanjar Wiro Sasmito}, title = {Sistem Pakar Diagnosis Hama dan Penyakit Tanaman Hortikultura dengan Teknik Inferensi Forward dan Backward Chaining}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {5}, number = {2}, year = {2017}, keywords = {expert system; forward chaining; backward chaining; rule-based horticulture}, abstract = { One of the obstacles to doing cultivation of horticulture plant is to overcome pest and disease. Pest and disease attack can decrease productivity and even causes harvest fail that influence toward one of income sources in the country. Therefore the diagnose on pest and disease must be done fastly and accurately. One of horticulture plant is red onion and chili plant. An expert system is offered as the second choice after expert on consultation. Using Expert System Development Life Cycle (ESDLC) method, combination inference engine and backward chaining for diagnosing pest and horticulture plant disease created as giving the solution. The technique of reasoning used in this research is rule-based. The result of the research is an application that can be used to diagnosis pest and disease horticulture plant, that are red onion and chili. By this application, the farmer can determine quick action should be taken if the farm pests and diseases, without waiting for a consultation with an expert to do the handling. The application result also could be a learning system to the farmer about pest and disease horticulture plant. }, issn = {2338-0403}, pages = {69--74} doi = {10.14710/jtsiskom.5.2.2017.70-75}, url = {https://jtsiskom.undip.ac.id/article/view/12901} }
Refworks Citation Data :
One of the obstacles to doing cultivation of horticulture plant is to overcome pest and disease. Pest and disease attack can decrease productivity and even causes harvest fail that influence toward one of income sources in the country. Therefore the diagnose on pest and disease must be done fastly and accurately. One of horticulture plant is red onion and chili plant. An expert system is offered as the second choice after expert on consultation. Using Expert System Development Life Cycle (ESDLC) method, combination inference engine and backward chaining for diagnosing pest and horticulture plant disease created as giving the solution. The technique of reasoning used in this research is rule-based. The result of the research is an application that can be used to diagnosis pest and disease horticulture plant, that are red onion and chili. By this application, the farmer can determine quick action should be taken if the farm pests and diseases, without waiting for a consultation with an expert to do the handling. The application result also could be a learning system to the farmer about pest and disease horticulture plant.
Article Metrics:
Last update:
Cassava Disease Classification Using Squeezenet CNN Technique
Identification of Tomato Plant Diseases by Leaf Image Using Squeezenet Model
Last update: 2024-11-06 04:41:08
Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.
