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All Journal JURNAL ILMIAH INFORMATIKA
Gilang Fadhillah Ramadhan
Universitas Stikubank
Computer Science & IT

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SISTEM DIAGNOSA PENYAKIT IKAN MENGGUNAKAN METODE CASE BASED REASONING DENGAN ALGORITMA SIMILARITAS SORGENFREI DAN K-NEAREST NEIGHBOR Gilang Fadhillah Ramadhan; Edy Winarno
JURNAL ILMIAH INFORMATIKA Vol 10 No 01 (2022): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v10i01.4634

Abstract

The increasing interest in betta fish lately has triggered many people to cultivate betta fish, and the prospects for the future are quite promising every year because they always increase profits. But behind that, betta fish care is not easy because betta fish are animals that are susceptible to disease. To improve the quality of Betta fish and reduce mortality due to disease, experienced fishery experts are needed. Many cultivators are still confused in dealing with betta fish that are attacked by diseases, for that a system was created that can help betta fish farmers recognize betta fish diseases by creating an expert system. The method used is Case-Based Reasoning using the similarity algorithm Sorgenfrei and coupled with K-Nearest Neighbor. This second method and algorithm can be used to diagnose the disease from the symptoms in the database. Based on the research that has been carried out, the results of consultation by the user by selecting some of the symptoms experienced produce a similarity value of 0.8695 and the system will provide a solution according to the disease.
Co-Authors Edy Winarno