<![CDATA[Flowers are an important raw material in the pharmaceutical and cosmetic industries. However, manual flower classification requires special skills, is time-consuming, and is prone to inconsistency. This study proposes the use of Machine Learning (ML) technology, especially the Support Vector Machine (SVM) method, to automate the flower classification process. The Gray Level Co-occurrence Matrix (GLCM) is a method used in extracting visual features of flowers and will obtain parameters such as contrast, correlation, energy, and homogeneity. The research stages include data collection, image preprocessing, feature extraction, classification model creation, and model performance evaluation using a confusion matrix. The results show that the classification model built is able to achieve an optimal accuracy of 78.3%. This approach shows great potential in improving the efficiency and consistency of automatic flower classification.]]>
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