Universitas Dian Nuswantoro, Indonesia
BibTex Citation Data :
@article{JMASIF52266, author = {Kevin Febrianto and Erika Udayanti and Bonifacius Indriyono and Wildan Mahmud and Iqlima Zahari}, title = {Expert System for Detection of Diseases in Layers Using Forward Chaining and Certainty Factor Methods}, journal = {Jurnal Masyarakat Informatika}, volume = {14}, number = {2}, year = {2023}, keywords = {expert system; forward chaining; certainty factor}, abstract = { Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations. }, issn = {2777-0648}, pages = {80--95} doi = {10.14710/jmasif.14.2.52266}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/52266} }
Refworks Citation Data :
Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations.
Article Metrics:
Last update:
Last update: 2024-12-25 22:18:29
The authors who submit the manuscript must understand that the article's copyright belongs to the author(s) if accepted for publication. However, the author(s) grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors should also understand that their article (and any additional files, including data sets, and analysis/computation data) will become publicly available once published under that license. See our copyright policy. By submitting the manuscript to Jmasif, 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. Jmasif will not be held responsible for anything arising because of the writer's internal dispute. Jmasif 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. Jmasif allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and Jmasif 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.
