Analisis Komparasi Pencocokan Pola Citra Jenis Ikan Mujair (Oreochromis Mossambicus) Menggunakan Algortima Scale Invariant Feature Transform Dengan Algoritma k-Nearest Neighbor

Suhadi ., Rudi Budi Agung, Syamsul Bahri

Abstract

Fish is a very large source of protein, by eating fish it can be healthier and participate in educating the nation's future generations, so it must be preserved. Fish is a food commodity that is easily available in Indonesia, and the price is also affordable. Tilapia fish (Oreochromis Mossambicus) is a popular consumption fish in Indonesia found in rivers, lakes, and lakes with a salt content of less than 0.05% for breeding. Tilapia fish is widely consumed by the public as a cheap and delicious fish that is often found in traditional and modern markets. This fish is often sold fresh or through the process of freezing (frozen). Previous research used the K-Nearest Neighbor (K-NN) algorithm and Image Processing to detect fish species using a smartphone. The purpose of this study was to analyze the comparison between the Scale Invariant Feature Transform (SIFT) Algorithm and the K-Nearest Neighbor (K-NN) Algorithm to determine the matching image patterns of Mujair fish species. The conclusion of this study is that the SIFT algorithm is the most accurate with a sampling error of 0.31% and the k-NN algorithm with a sampling error of 69.89%.

Keywords

Algortima SIFT, Algoritma k-NN, Citra Digital, Komparasi

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