<![CDATA[The diversity of Indonesian traditional houses represents a cultural heritage that must be preserved. However, the lack of interest among younger generations and the difficulty in recognizing the distinctive architectural characteristics of traditional houses present challenges to preservation efforts. This study aims to develop an image classification model for Indonesian traditional houses using a hybrid CNN-SVM approach to improve recognition accuracy. The dataset consists of 3,919 images from five classes of traditional houses, namely gadang, joglo, panjang, tongkonan, and honai, with an 80% training split, 10% validation, and 10% testing. The data were processed through resizing, augmentation, and normalization before being trained using a CNN architecture with five convolutional layers as a feature extractor and an SVM serving as a multi-class classifier. The experimental results show that the hybrid CNN-SVM model achieved an accuracy of 96.68%, with consistently high precision, recall, and F1-score across all classes. These findings demonstrate that integrating CNN as a feature extractor and SVM as the final classifier can enhance the model’s generalization capability in distinguishing images of Indonesian traditional houses.]]>
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