Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI
M Sadewa Wicaksana Wibowo
Universitas Muhammadiyah Lamongan
Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Public Health

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
An EfficientNetV2-Based for Alzheimer’s Disease Classification M Sadewa Wicaksana Wibowo; Khairul Umam
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5311

Abstract

In Indonesia, Alzheimer’s disease has emerged as a critical public health priority. This neurodegenerative disorder is characterized by the gradual erosion of memory, linguistic capabilities, and problem-solving skills resulting from irreversible neuronal damage. Magnetic Resonance Imaging (MRI) is commonly used for early diagnosis; however, manual interpretation of MRI scans is time-consuming and subject to inter-observer variability among medical professionals. Recent advances in artificial intelligence have enabled automated analysis of MRI images for Alzheimer’s disease detection, yet many existing approaches rely on deep learning architectures with high computational complexity. To address this limitation, this study proposes a lightweight deep convolutional network based on EfficientNetV2 for Alzheimer’s disease classification using brain MRI images. Data augmentation techniques, including random rotation, affine transformation, horizontal and vertical flipping and normalization are applied to enhance model generalization. Two EfficientNetV2 variants, EfficientNetV2_s and EfficientNetV2_m, are evaluated and compared using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate that EfficientNetV2_s achieves superior performance, attaining an accuracy, precision, recall, and F1-score of approximately 0.90, while EfficientNetV2_m achieves corresponding values of approximately 0.81, indicating lower generalization capability. These results confirm that the smaller EfficientNetV2_s model provides more accurate and reliable classification performance despite its reduced computational complexity.Keywords - Alzheimer’s Disease, Classification, Convolutional Neural Networks, Deep Learning.
Co-Authors Khairul Umam