Advancing Winged Animal Classification Through Image Analysis - Prasetya Triputra Nugraha; Imam Yuadi (Jurnal Penelitian Pendidikan, Psikologi Dan Kesehatan (J-P3K), 2026)
This study is to assess how well two classification algorithms, Support Vector Machine (SVM) and Logistic Regression, work with deep learning-based feature extraction techniques, including Inception V3, VGG-16, and VGG-19. The methodology comprised preprocessing a collection of photos of flying animals, using the three convolutional neural network (CNN) designs to extract features, and applying the two algorithms to do classification. AUC, Classification Accuracy (CA), F1 Score, Precision, Recall, and MCC were among the important metrics used to assess the models. According to the findings, Inception V3 performed better than VGG-16 and VGG-19 on every parameter, with Logistic Regression obtaining nearly flawless scores (AUC = 1.000, CA = 0.987, F1 = 0.987). Although it was marginally less effective than Logistic Regression (AUC = 0.998, CA = 0.943, F1 = 0.946), SVM also did well with Inception V3. The feature extraction techniques that performed the worst were VGG-16 and SVM in particular (CA = 0.890, F1 = 0.891). These results highlight the effectiveness of Logistic Regression for classification in this setting and the improved multi-scale feature extraction capabilities of Inception V3. This study demonstrates how effective classifiers and cutting-edge CNN architectures, such as Inception V3, may be combined to automatically classify winged animals.