<![CDATA[The increasing volume of waste each year poses a serious challenge in waste management, particularly in the waste sorting process, which remains suboptimal. The lack of public awareness and limited manual sorting facilities are major obstacles to creating an effective waste management system. To address this issue, this study developed a waste classification system based on visual images by utilizing the Xception algorithm integrated with the Convolutional Block Attention Module (CBAM) to improve classification accuracy. The dataset used in this study includes various categories of organic and anorganic waste. The experiments involved several stages, including the integration of CBAM into the Xception architecture, testing different data splitting schemes for training and validation, and hyperparameter tuning using the Random Search method with 10 combinations. The model was trained using the Keras and TensorFlow libraries, and the trained model was saved in the .h5 format commonly used for deploying deep learning models into web applications. The results showed that the addition of CBAM improved the model's accuracy from 88.38% to 91.29% without significantly increasing training time. Furthermore, the best hyperparameter combination obtained from tuning was Dense = 128, Dropout = 0.3, Optimizer = Adam, and Learning Rate = 0.0001. When retrained using this configuration, the model achieved a highest accuracy of 93.37%. The best-performing model was then integrated into a Flask-based web application. This application allows users to upload images of waste through a simple web interface and instantly receive the predicted waste type classification. With the implementation of this technology, the system is expected to assist the public in sorting waste more easily and to increase active participation in environmentally conscious waste management. Keyword: Waste Classification, Xception, CBAM, Deep Learning, Flask]]>
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