<![CDATA[The quality of swiftlet nests is a key factor in determining the market value and quality standards of this commodity in both domestic and international markets. The quality classification process, which is currently dominated by manual methods, has fundamental weaknesses, namely high subjectivity and inconsistency in sorting results. This study aims to evaluate the performance of deep learning architectures in automatically classifying the quality of swiftlet nests based on visual characteristics. The main contribution of this study is to address the research gap in previous publications by strictly aligning quality class labels with the formal document of the Indonesian National Standard (SNI) 8998:2021, as well as presenting a cross-architecture comparative analysis to map model performance trade-offs. Evaluations were conducted on the MobileNetV2, and presents a cross-architecture comparative analysis to map model performance trade-offs. Evaluations were conducted on the MobileNetV2, ResNet50, and YOLOv8n-cls architectures using accuracy, precision, recall, and F1-score metrics. The research dataset includes visual images of swiftlet nests grouped into three quality classes (good, moderate, and poor) through self-documentation and augmentation techniques. Test results show that YOLOv8n-cls achieved the highest performance in this scenario with an accuracy of 99.5%, precision of 98.78%, recall of 98.72%, and an F1-score of 98.71%. Meanwhile, MobileNetV2 achieved a competitive accuracy of 98.37% with good computational efficiency, while ResNet50 demonstrated the lowest performance (66% accuracy) due to network complexity on the limited dataset. This research indicates that lightweight architectures exhibit good stability for limited-size visual datasets; however, external validation using larger datasets remains necessary to test the models’ generalization capabilities more broadly.]]>
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