Implementasi Euclidean Distance dan Segmentasi K-Means Clustering Pada Identifikasi Citra Jenis Ikan Nila
DOI:
https://doi.org/10.30865/klik.v3i1.551Keywords:
Euclidean Distance; Shape Feature Extraction; Texture Feature Extraction; K-Mean Clustering; Tilapia FishAbstract
Tilapia is one of the favorite fish for consumption because it contains high nutrition at a relatively low price. This is what makes fish cultivators in Indonesia choose tilapia for cultivation. Tilapia has several varieties that have different characteristics, thus affecting the way of handling and cultivating these fish. For this reason, tilapia cultivators need to have knowledge about the types of tilapias so they can cultivate based on the characteristics of these fish species. This study aims to implement the Euclidean Distance algorithm and image segmentation with K-Mean Clustering on image identification of tilapia species based on their shape and texture characteristics. The K-Mean Clustering algorithm is used to separate the foreground and background in the image. Furthermore, the object's characteristics will be extracted based on its shape and texture characteristics. Furthermore, the identification process is carried out using the Euclidean Distance algorithm which will look for similarity values between two or more by calculating the value of the distance from Euclidean, to determine whether the object is included in which class based on the closeness of the values obtained. Based on the test results, the accuracy value reached 84.3%. These results show that the developed model can identify tilapia species well
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