PERBANDINGAN KLASIFIKASI KACANG KERING DENGAN ALGORITMA KNN DAN SVM LINEAR
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Abstract
Advancements in technology have led to the creation of new types of beans, making their classification more difficult. However, the classification of dry beans can be simplified using machine learning algorithms such as Support Vector Machine and K-Nearest Neighbor. The objective of this study was to identify and differentiate types of beans and determine the algorithms that yield the best accuracy, precision, recall, and f1-score. The dataset used for this research was obtained from the Kaggle website and consists of 16 features and 1 class for classification. The findings reveal that the K-Nearest Neighbor algorithm outperformed the Support Vector Machine algorithm in terms of classification accuracy, regardless of the division between training and test data. Thus, K-Nearest Neighbor provides better results for the classification of beans in this study.
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