Identifikasi Kualitas Kesegaran Ikan Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Ekstraksi Ciri Warna Hue, Saturation, Value (HSV)

JERANDU, Charmelia Yunizar (2023) Identifikasi Kualitas Kesegaran Ikan Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Ekstraksi Ciri Warna Hue, Saturation, Value (HSV). Undergraduate thesis, Universitas Katolik Widya Mandira Kupang.

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Abstract

Fish has a very high nutritional content and is needed by the human body, such as a protein. With the increasing production, and need for consumption of good and fresh fish, irresponsible sellers take advantage of this situation by selling fish that are not fit for consumption, such as fish that are not fresh (rotten), fish that contain chlorine and formalin which can be detrimental to consumers. The purpose of this study was to determine how accurate the identification of fish freshness quality was using Hue, Saturation, Value (HSV) color feature extraction. The research method used is K-Nearest Neighbor (KNN) and is classified into several parts, namely, data collection techniques, needs analysis, design and training then tested. The image sample data used in this study totaled 320 images consisting of fresh and non-fresh fish images, which will then be divided into training data and test data. The training data sample consisted of 280 images divided into 140 fresh fish images and 140 non-fresh fish images, while the test data sample consisted of 40 images divided into 20 fresh fish images and 20 non-fresh fish images. Analysis of color features was carried out on the gills and head or the area around the eye of the fish using Red, Green and Blue (RGB) colors which would be converted into the Hue, Saturation, Value (HSV) color space for the extraction and training process to obtain results. The results showed that the use of HSV color feature extraction was successfully applied with an accuracy value of 97.50% for training and 92,50% for testing.

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