IMPLEMENTASI METODE KLASIFIKASI UNTUK IDENTIFIKASI JENIS KACANG KERING BERDASARKAN FITUR MORFOLOGI
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Abstract
Identification of dry bean species is a critical challenge in the agricultural industry, especially to ensure the quality and optimal utilization of the product. This study focuses on the application of a morphological feature-based classification method to automatically recognize dry bean species. Features such as size, shape, surface texture, and color are used as the basis for classification. Several classification algorithms, including K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Decision Tree, were tested to find the most effective algorithm in identifying the types of beans. The research dataset includes various types of dry beans with different morphological variations. The experimental results show that the morphological feature-based classification approach is able to achieve a high level of accuracy, with Artificial Neural Network (ANN) being the algorithm that shows the best performance. The implementation of this method is expected to provide practical solutions in managing the quality and processing of dry beans in the agricultural sector.
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This work is licensed under a Jurnal Komunikasi Creative Commons Attribution-ShareAlike 4.0 International License.
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