Classification of Roronoa Zoro Anime, Cosplay, and Action Figure Images Using VGG16 and Inception V3 with Logistic Regression and Support Vector Machine to Improve Popular Culture Object Recognition - Noor Rizki, Denaldy Oktavian; Yuadi, Imam (Informatika, Universitas Jenderal Soedirman, 2026)
The diversity of visual representations of anime characters across anime scenes, cosplay photographs, and action figure images poses challenges for automated image classification due to variations in pose, lighting, background, and visual style. This study aims to develop a robust image classification system for the character Roronoa Zoro using deep learning–based feature extraction combined with classical classification algorithms. The method employs VGG16 and Inception V3 as feature extractors, followed by classification using Logistic Regression and Support Vector Machine. The dataset comprises three classes (anime, cosplay, and action figure), processed through image resizing, normalization, and data augmentation. Performance was evaluated using accuracy, F1-score, Area Under Curve (AUC), Matthews Correlation Coefficient (MCC), confusion matrix, silhouette plot, and multidimensional scaling. The experimental results show that Inception V3 combined with Logistic Regression achieved the best performance, with an AUC of 0.993, accuracy of 95.7%, F1-score of 0.957, and MCC of 0.935, outperforming VGG16 with Logistic Regression, which achieved 91.7% accuracy and an AUC of 0.986. Visualization-based evaluation indicates that Inception V3 produces more separable feature representations, particularly in distinguishing cosplay images from anime and action figure categories. This research demonstrates the effectiveness of multi-model feature extraction and classification for improving recognition performance in character-based image classification tasks and contributes empirically to the application of hybrid deep feature–machine learning approaches in computer vision.