Abstract: Automated waste classification using computer vision has become essential for improving environmental sustainability and reducing manual sorting effort. This study presents an enhanced waste image classification model based on EfficientNet-B0, trained using a two-stage transfer learning strategy that combines feature extraction and fine-tuning. The proposed approach aims to enhance classification accuracy while maintaining computational efficiency. Experimental evaluations conducted on a heterogeneous multi-class waste dataset demonstrate the superiority of the proposed method. The confusion matrix results indicate a high proportion of correct predictions across most categories, with only minor misclassifications among visually similar classes, such as metal and paper. The model's robustness is further validated through 5-Fold Cross-Validation, which yields an average accuracy of 94.3% with a standard deviation of ±0.007, confirming consistent performance across data partitions. Compared with state-of-the-art CNN architectures, including ResNet50 and DenseNet121, the proposed model achieves the highest accuracy while using the fewest parameters (4.38M), making it suitable for deployment in resource-constrained environments. Additionally, qualitative analysis using Grad-CAM confirms that the model’s decisions are explainable and based on relevant object features. These findings demonstrate that the proposed EfficientNet-B0 model constitutes a reliable, efficient, and interpretable solution for automated waste classification. The model is further evaluated using cross-validation and explainable AI (Grad-CAM) to assess both performance stability and interpretability.

Fuzy Yustika Manik, Pauzi Ibrahim Nainggolan, T. H. F Harumy, Dewi Sartika Br Ginting, Aini Maharani, Hafizha Ramadayanti, Jessica Almalia and Muhammad Putra Harifin. “Explainable CNN-Based Multiclass Household Waste Classification Using Grad-CAM for Smart Waste Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170155

@article{Manik2026,
title = {Explainable CNN-Based Multiclass Household Waste Classification Using Grad-CAM for Smart Waste Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170155},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170155},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Fuzy Yustika Manik and Pauzi Ibrahim Nainggolan and T. H. F Harumy and Dewi Sartika Br Ginting and Aini Maharani and Hafizha Ramadayanti and Jessica Almalia and Muhammad Putra Harifin}
}