IJATIS: Indonesian Journal of Applied Technology and Innovation Science

Effective Machine Learning Schemes for Face Recognition in Higher-Dimensional Datasets

Onibeju Basit Adeyinka — 2026-07-31

Face recognition systems play a vital role in personal identification, particularly in security applications. However, high-dimensional image data often reduces the effectiveness of machine learning (ML) models by increasing computational complexity and training difficulty. This study aims to improve face recognition performance by combining dimensionality reduction and deep feature extraction techniques. Two benchmark datasets, Filtered LFW and a custom VGGFace2 dataset, were used to evaluate two ML-based models. The first model integrates Linear Discriminant Analysis (LDA), CNN (ResNet18), and K-Nearest Neighbors (KNN), while the second combines CNN (ResNet18) with Naïve Bayes (NB). Both models were trained, tested, and evaluated using accuracy, precision, recall, F1-score, and execution time. On the LFW dataset, the CNN-LDA-KNN model achieved the best performance, with 97.82% accuracy, 0.9799 precision, 0.9782 recall, 0.9782 F1-score, and 68.85 seconds evaluation time, outperforming the CNN-NB model (96.71% accuracy). Similarly, on the VGGFace2 dataset, CNN-LDA-KNN obtained 93.46% accuracy compared with 78.87% for CNN-NB. These findings demonstrate that integrating LDA with CNN-based feature extraction and KNN classification significantly enhances face recognition performance on high-dimensional image datasets while maintaining competitive computational efficiency.

Application of Deep Learning for the Classification of Brain Tumor Magnetic Resonance Imaging Images

Rizki Andreas — 2026-07-28

Accurate and timely classification of brain tumors from Magnetic Resonance Imaging (MRI) is essential for supporting clinical diagnosis and treatment planning. This study presents a comparative evaluation of transfer learning-based Convolutional Neural Network (CNN) architectures, including ResNet-50, DenseNet121, and EfficientNet-B2, for binary brain tumor MRI classification (Glioma and Meningioma). The dataset was augmented to improve model generalization, and the data were split into training and test sets using an 80:20 hold-out split. The models were trained using Adam and RMSProp optimizers with different learning rates, and their performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that data augmentation significantly enhances classification performance across all evaluated architectures. Among the tested models, ResNet-50 with the RMSProp optimizer and a learning rate of 0.001 achieved the best performance, yielding the lowest training and validation losses and perfect classification results on the test set, with accuracy, precision, recall, and F1-score approaching 100%. DenseNet121 and EfficientNet-B2 also achieved excellent performance but were slightly inferior to ResNet-50. These findings indicate that transfer learning with ResNet-50 and RMSProp provides an effective and reliable solution for automated brain tumor MRI image classification