Klasifikasi Tanaman Calathea Menggunakan Metode SVM Berbasis Fitur HSV dan HOG
Keywords:
Calathea, support vector machine, HSV, HOG, image classificationAbstract
The visual similarity among Calathea varieties—especially in leaf color, pattern, and texture, often causes misidentification by the general public and even horticulturists. This research proposes an automated classification system for five Calathea varieties (Black Lipstick, Corona, Crimson, Medallion, and Pink Jessy) using Support Vector Machine (SVM) based on combined HSV (Hue, Saturation, Value) and HOG (Histogram of Oriented Gradients) features. A dataset of 1,000 leaf images was collected under controlled conditions (white background, fixed distance, consistent lighting) and processed through preprocessing, HSV-based color segmentation, grayscale conversion, and HOG feature extraction. The SVM model with a linear kernel was evaluated using 5-fold cross-validation. The system achieved an overall average accuracy of 93.52%, with Calathea Corona showing the highest accuracy (96.50%) and precision (95.12%), while Calathea Black Lipstick recorded the lowest precision (67.50%) but the highest recall (92.00%). These results demonstrate that the fusion of color and shape features enhances classification performance, although complex leaf patterns can still pose challenges in precision. This approach offers a practical and objective solution for Calathea identification in horticultural applications.
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