Website-Based Boycott Product Detection System using Convolutional Neural Network: A comparison of YOLOv8 and VGG16 - Mariani Ani; Dolly Indra Indra; Sitti Rahma Jabir Rahma (yocto brain, 2026)
Introduction: Product boycotts have become a common form of social response in the digital era, yet manual identification of boycotted products can be slow and inaccurate. This study develops a website-based system and compares YOLOv8 and VGG16 for automated identification of boycott and non-boycott products from images. Method: A dataset of 4,250 food and beverage product images, comprising 3,638 boycott and 612 non-boycott products, was collected from internet sources and divided into 70% training, 20% validation, and 10% testing sets. YOLOv8 was trained using 640×640-pixel inputs as a one-stage object detector, while VGG16 used 224×224-pixel inputs with transfer learning as an image classifier. Both models were integrated into a website-based detection system and evaluated using accuracy, precision, recall, and F1-score. Results and Discussion: On 425 test images, YOLOv8 achieved 91.7% accuracy, 99.7% precision, 90.3% recall, and a 94.9% F1-score, substantially outperforming VGG16, which achieved 51.3% accuracy, 81.6% precision, 55.7% recall, and a 66.2% F1-score. YOLOv8 demonstrated greater robustness to variations in background, lighting, and product appearance because of its object-localization capability. Conclusion: YOLOv8 is more effective than VGG16 for website-based boycott product detection and provides a stronger foundation for practical real-time identification systems.