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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
SILA MILDAWATI
UNIVERSITAS MERCUBUANA YOGYAKARTA
Computer Science & IT Engineering

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ANALISIS PENINGKATAN PERFORMA CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN HYPERPARAMETER TUNING DAN ENSEMBLE LEARNING PADA KLASIFIKASI CITRA MRI TUMOR OTAK SILA MILDAWATI; Putri Taqwa Prasetyaningrum
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8341

Abstract

This study aims to improve brain tumor MRI image classification performance through selected hyperparameter combinations and ensemble learning. The publicly available dataset consists of 3,264 training images and 394 testing images categorized into four classes: glioma, meningioma, no tumor, and pituitary tumor. The preprocessing stage includes resizing images to 224 × 224 pixels, normalization, and training-data augmentation. Two pre-trained CNN architectures, VGG16 and EfficientNetV2B0, were fine-tuned using selected combinations of the Adam and SGD optimizers, learning rates of 0.0001 and 0.001, and batch sizes of 32. The best configuration was obtained using Adam, a learning rate of 0.0001, and a batch size of 32. VGG16 achieved an accuracy of 90.00%, while EfficientNetV2B0 reached 98.73%. Combining the prediction probabilities of both models using soft-voting ensemble learning increased the accuracy to 99.49%, with two misclassified images. These results indicate that an appropriate training configuration and soft-voting ensemble learning can numerically improve MRI-based brain tumor classification performance.
Co-Authors Prasetyaningrum, Putri Taqwa