Klasifikasi Kematangan Buah Alpukat Mentega Menggunakan Metode K-Nearest Neighbor Berdasarkan Warna Kulit Buah
DOI:
https://doi.org/10.30865/resolusi.v3i5.763Keywords:
Avocado; Artificial Intelligence; Classification; K-Nearest Neighbor; KNNAbstract
This study offers a system to be able to calcify the ripeness level of avocado butter using the K-Nearest Neighbor method based on fruit skin color. The purpose of this study was to test the accuracy of the optimization of the k-nearest neighbor algorithm on the classification of butter avocado maturity levels. The problems raised in this study are to make it easier for farmers and traders of avocado butter in determining or sorting avocado butter based on the level of ripeness as seen from the color of the fruit skin for further classification. Based on the results of research using the K-Nearest Neighbor method by examining the skin color of 20 butter avocado samples, consisting of 7 unripe butter avocados, 6 half-ripe butter avocados, and 7 ripe butter avocados, the results obtained an accuracy rate of 80. %. The results of sample testing conducted on unripe butter avocados illustrated an accuracy rate of 85.71%. In contrast to unripe butter avocados, the results of testing half-ripe butter avocados showed an accuracy rate of the K-Nearest Neighbor method of 66.66%. There is a significant difference in the level of accuracy for the two types of butter avocado maturity of 19.05%.
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