Determination Of Freshwater Consumption Fish Diseases Using Artificial Neural Network Methods
DOI:
https://doi.org/10.31316/astro.v3i1.6217Abstract
Artificial Neural Network (ANN) is a branch of Artificial Intelligence (AI) that uses neuron-based computational methods to identify and solve problems. In this study, we tried to use ANN to identify diseases of freshwater fish commonly consumed by the public, using one of the ANN methods, namely backpropagation. This research produces a website-based system that uses the backpropagation Neural Network method so that it can be used to help freshwater fish breeders or cultivators identify fish diseases that are kept more accurately than conventional methods. In addition, it is hoped that this system can anticipate more severe infections in fish belonging to cultivators. The results of system testing show that regarding the appearance of the system, 38.5% of respondents answered that the design was beautiful, related to the ease of use of the system, 46.2% of respondents answered that the approach was straightforward to use, regarding the performance of the system 53.8% of respondents answered that the system performance was excellent. Regarding the system's benefits, 76.9% of respondents answered that the system was beneficial
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2024 Bagas Apriyanto, Ahmad Riyadi, Ari Kusuma Wardana, Mohd Nasrun Mohd Nawi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The journal allow the authors to hold the copyright without restrictions and allow the authors to retain publishing rights without restrictions.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).