Classification of Urban Images vs. Green Spaces Images Using SVD and KNN - Satria, Ryan (JBASE - Journal of Business and Audit Information Systems, 2026)
This study aims to classify urban and green land images by employing the Singular Value Decomposition (SVD) method for dimensionality reduction and the K-Nearest Neighbors (KNN) algorithm for classification. The dataset used consists of 600 images (300 urban and 300 green land) with a resolution of 256x256 pixels, sourced from the Kaggle "Aerial Landscape Images" dataset. Each image was transformed into a feature vector, then reduced using SVD, where this study compares the use of 5 components and 20 components. The dataset was subsequently divided into 80% training data and 20% testing data for classification using KNN with k=3. Performance evaluation was conducted via confusion matrix and the calculation of accuracy, precision, recall, and F1-score. The results showed that the model with 5 SVD components achieved the highest accuracy of 95.00%, outperforming the 20-component model (91.67%). This finding demonstrates the effectiveness of SVD-KNN, but shows that a higher number of components can degrade performance. The limitation of the purely color-based method was also identified during testing on "residential area" images, which possess overlapping features between “Urban” and “Green Spaces”.