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Classification of apple maturity based on color using the K-Nearest Neighboor (KNN) method
Abstract
Purpose: The aim of this research is to provide support to apple fans and farmers in determining the choice of fruit that is ripe and ready to be consumed, using indicators of outer skin color as a basis for classification.
Design/methodology/approach: The approach uses the K-Nearest Neighbor (KNN) method to classify the level of ripeness of apples based on skin color. KNN is used as a classification method. This approach utilizes the similarity of skin color with training data to determine the level of maturity. The evaluation results showed an accuracy of 90%, making it an effective approach for identifying the ripeness level of apples.Findings/result: From the results of the system evaluation of 206, it shows an accuracy level of 90% with a sensitivity of 80% and a specificity of 67% as measured by the Hold Out Estimation model.
Originality/value/state of the art: This research uses test data/testing data originating from Kaggle and Google as well as several photos taken directly. In total, 206 images of apples were used.
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DOI: https://doi.org/10.31315/telematika.v21i1.11773
DOI (PDF): https://doi.org/10.31315/telematika.v21i1.11773.g6345
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Status Kunjungan Jurnal Telematika