PENGGUNAAN K-NEAREST NEIGHBOR (KNN) UNTUK MENGKLASIFIKASI CITRA BELIMBING BERDASARKAN FITUR WARNA
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Institusion
Universitas Pembangunan Nasional Veteran Jakarta
Author
Duwen Imantata Muhammad, .
Subject
Z719 Libraries (General)
Datestamp
2021-06-23 08:05:18
Abstract :
There are still many who do not know the exact level of fruit maturity. As a result, sellers and buyers find it difficult to estimate the level of fruit maturity, especially star fruit. Starting from this problem we need a system that can distinguish the level of maturity of the fruit. Based on this, the purpose of this study was conducted to identify the maturity level of star fruit based on the image with the K-Nearest Neighbor algorithm and feature extraction of Hue saturation Value (HSV) using the Matlab program to assist the process of digital image processing. By using the KNN algorithm obtained an accuracy of 93.33% in the experiment using the value K = 7
There are still many who do not know the exact level of fruit maturity. As a result, sellers and buyers find it difficult to estimate the level of fruit maturity, especially star fruit. Starting from this problem we need a system that can distinguish the level of maturity of the fruit. Based on this, the purpose of this study was conducted to identify the maturity level of star fruit based on the image with the K-Nearest Neighbor algorithm and feature extraction of Hue saturation Value (HSV) using the Matlab program to assist the process of digital image processing. By using the KNN algorithm obtained an accuracy of 93.33% in the experiment using the value K = 7
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