Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences
Rahman, Abdul Wahab Abdul
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Sukmana, Husni Teja; Hasibuan, Zainal Arifin; Rahman, Abdul Wahab Abdul; Bayuaji, Luhur; Masruroh, Siti Ummi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Evaluating the Security of Electronic Medical Records in Indonesia’s SIMPUS Application Using the CIA Framework Durachman, Yusuf; Rahman, Abdul Wahab Abdul
International Journal of Informatics and Information Systems Vol 8, No 3: September 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i3.263

Abstract

Ensuring the security of electronic medical records (EMRs) is a critical challenge in the digital transformation of healthcare systems, particularly in developing countries. This study evaluates the security of Indonesia’s Community Health Center Information System (SIMPUS) based on the principles of confidentiality, integrity, and availability (CIA). A qualitative descriptive approach was employed, combining interviews and direct observation of SIMPUS implementation across multiple user roles. The findings reveal that while confidentiality is supported through user authentication, vulnerabilities remain due to shared account usage and the absence of automatic log-off features. Data integrity is maintained through restricted editing rights, but the lack of an audit trail limits the system’s ability to detect unauthorized changes. Data availability is generally sufficient; however, reliance on manual backup processes exposes the system to potential data loss. The study highlights the need for enhanced audit mechanisms, automated backup solutions, and staff training to strengthen data security compliance with national regulations and international standards such as ISO 27001 and HIPAA. Strengthening these measures will help ensure that SIMPUS can function as a secure and reliable platform for managing electronic medical records in Indonesia’s primary healthcare system.
Co-Authors Husni Teja Sukmana Luhur Bayuaji, Luhur Siti Ummi Masruroh Yusuf Abdurachman Zainal Arifin Hasibuan