APPLICATION OF K-NEAREST NEIGHBOR (KNN) METHOD TO DETERMINE CUPANG FISH USING CANNY EDGE DETECTION AND INVARIANT MOMENT

Fajar Shidiq; Eka Wahyu  Hidayat; Neng Ika  Kurniati

doi:10.20884/1.jutif.2022.3.1.95

Fajar Shidiq Program Studi Teknik Informatika, Universitas Siliwangi, Indonesia
Eka Wahyu Hidayat Program Studi Teknik Informatika, Universitas Siliwangi, Indonesia
Neng Ika Kurniati Program Studi Teknik Informatika, Universitas Siliwangi, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.1.95

Keywords: Betta Fish, K-Nearest Neighbor, Canny Edge Detection, Invariant Moment

Abstract

Betta fish are known as fighting fish, are aggressive and like to attack several types of Betta fish. They have attractive body colors, beautiful fins, calm and dignified movements. However, there are still many people who are wrong in distinguishing the types of betta fish, especially for those who just bought them, so to help determine the type of betta fish, a Matlab application was built to determine the types of betta fish based on their shape. K-Nearest Neighbor can classify objects based on learning data that is closest to the object so that the results can be more accurate. Canny is known as the optimal edge detection, this algorithm provides a low error rate. Invariant Moment is a feature extraction method that produces 7 features used to recognize an object. The combination of K-Nearest Neighbor, Canny, and Invariant Moment resulted in a fairly high accuracy for determining the type of betta fish, namely an average of 68.5714%, for training data with a total of 70 betta fish data, and 70%, for test data with the number 20 Betta fish data.

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