Classification Of Egg Quality Using The K-Nearest Neighbor Algorithm In Machine Learning
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5471Abstract
In addition to meat, fish, and milk, one of the staple foods consumed by the community is chicken eggs. Egg quality assessment is separated into two categories: exterior (egg shell) and interior (egg contents). However, the evaluation method used in this investigation is focused on evaluating the external quality of eggs. Pre-processing, feature extraction, classification, and evaluation are steps taken in the image processing method used to classify chicken eggs. Classification methods that can be used include the K-Means Clustering and K-Nearest Neighbor (KNN) methods and improved KNN. Based on the findings in the study, the KNN improvisation method can be used to classify chicken egg quality, with a test accuracy value of 91.67%.
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Copyright (c) 2024 Windy Marantika, Putri Romian Gultom, William Agustine, Tama Ulina br Sinuhaji, Siti Aisyah, Amalia Amalia, Muhammad Radhi
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