<![CDATA[Visual classification of salak (snake fruit) varieties remains challenging due to similarities in texture, color, and morphological characteristics across classes. Manual identification is prone to subjectivity and inconsistency in determining varieties. This study proposes an ensemble model based on fusion and stacking applied to deep learning feature extraction in order to improve the accuracy of salak variety classification. Image features are extracted using two pre-trained Convolutional Neural Network architectures, namely VGG16 and ResNet50, as deep feature extractors. The resulting feature representations are subsequently classified using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. The output probabilities of both classifiers are then combined through a stacking ensemble approach with Logistic Regression as the meta-learner. The dataset consists of 584 images distributed across four salak varieties. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the proposed fusion–stacking approach achieves an accuracy of 95%, outperforming single CNN-based models and conventional classification methods. These findings demonstrate that the integration of deep feature extraction and ensemble learning effectively enhances the discriminative capability of the model in agricultural image classification.]]>
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