CLASSIFICATION OF SALT QUALITY BASED ON COLOR AND SHAPE FEATURES USING THE K-NEAREST NEIGHBOR (KNN) METHOD
Keywords:
Digital Image Processing, Salt Quality Classification, K-Nearest Neighbor (KNN), Color Features, Shape Features.Abstract
The quality of salt crystals is one of the factors that determines the market value and quality of salt. The quality assessment process, which is still carried out manually, tends to be subjective because it relies on visual observation. This study aims to develop an image-processing-based salt crystal quality classification system using the K-Nearest Neighbor (KNN) method to support a faster, more objective, and consistent identification process. The research dataset consists of 600 salt crystal images divided into three categories: high, medium, and low quality, obtained from salt farmers in Pinggir Papas Village, Sumenep Regency. The research stages include preprocessing, segmentation, RGB and HSV color feature extraction, shape feature extraction, and classification using the KNN method. The system was implemented as a Streamlit-based application. The test results showed that the best model was obtained at K = 3, achieving an accuracy of 98.333% during the parameter selection process. Testing on 60 test images resulted in an accuracy of 86.67%, precision of 87.24%, recall of 86.67%, and an F1-score of 86.81%. These results indicate that the combination of color features, shape features, and the KNN method can classify salt crystal quality effectively and can therefore be used as an alternative supporting tool for the automated classification of salt quality
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Copyright (c) 2026 Ahmad Nur Wahid, Arda Gusema Susilowati, Rully Widiastutik (Author)
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