<![CDATA[Manual identification and classification of ornamental flower varieties is time-consuming and highly dependent onindividual expertise, resulting in identification errors that impact the production value chain and operational efficiency ofthe horticulture industry. This research aims to implement an automated classification system for three types ofornamental flowers (sunflower, rose, and tulip) using K-Means Clustering method with visual feature analysis to improveidentification accuracy and computational efficiency. The research methodology includes acquisition of 210 high-qualitybalanced flower images (70 samples per class), preprocessing with RGB to HSV color space transformation, segmentationusing K-Means with k=3, and extraction of 10 multi-dimensional features encompassing morphology, color, and GLCMtexture. The dataset was divided into 80% training and 20% testing using stratified sampling with K-Fold CrossValidation. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The researchresults demonstrate overall accuracy of 88.89% with sunflower achieving F1-score of 0.98 (0% error), rose 0.86 (14.3%error), and tulip 0.85 (19% error). Aspect ratio, solidity, and mean red channel intensity proved to be the mostdiscriminative features. Misclassification predominantly occurred in the rose-tulip pair (71.4%) due to red spectrum coloroverlap and morphological variation. K-Means algorithm demonstrated optimal balance between accuracy,computational efficiency (0.3s/image), and interpretability, although it has limitations on low feature separability. Thisstudy is limited to a small dataset (210 images) and controlled conditions, requiring real-world validation for bettergeneralization]]>
