Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/44389
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dc.contributor.authorRamadiani, Ramadiani-
dc.contributor.authorJundillah, Muhammad Labib-
dc.date.accessioned2023-01-08T23:35:56Z-
dc.date.available2023-01-08T23:35:56Z-
dc.date.issued2022-12-20-
dc.identifier.citationRAMADIANI, et al. 2022. "Selecting goldfish broods use the weighted product and simple additive weighting methods". IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 11, No. 4, Dec. 2022, pp. 1405~1413 https://ijai.iaescore.com/index.php/IJAI/article/view/21883en_US
dc.identifier.issn2089-4872/2252-8938-
dc.identifier.urihttp://repository.unmul.ac.id/handle/123456789/44389-
dc.description.abstractMajalaya carp is a freshwater fish that has important economic value and is widespread in Indonesia. Goldfish is the most cultivated fish because it has many advantages both physiologically and genetically. Several factors of assessment in the selection of superior brood stock that can be considered in the cultivation of goldfish cultivators are; ideal body weight, fish movement, physical deformities, scales, and the base of the tail. All of these factors can help in the decision-making process for superior goldfish. This study uses two methods, namely the simple additive weighting (SAW) method and the weighted product (WP) method. Based on the results of research that has been carried out on 20 superior broodfish of Majalaya goldfish, the level of accuracy is obtained by comparing with existing data. The WP method gets an accuracy value of 90% while the SAW method gets an accuracy value of 80%.en_US
dc.language.isoenen_US
dc.publisherIAES International Journal of Artificial Intelligence (IJ-AI) Vol. 11, No. 4, Dec. 2022, pp. 1405~1413en_US
dc.subjectDecision support systemen_US
dc.subjectMajalaya goldfishen_US
dc.subjectSimple additive weightingen_US
dc.subjectSuperior breederen_US
dc.subjectWeighted product;en_US
dc.titleSelecting goldfish broods use the weighted product and simple additive weighting methodsen_US
dc.typeArticleen_US
Appears in Collections:A - Computer Sciences and Information Technology

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