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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Digit : Digital of Information Technology Journal of Applied Data Sciences Jurnal Sisfotek Global Technologia Journal Integrated System and Management Technology Indonesian Journal of Infomatics Computer Architecture and Signal Processing Software Engineering in Computing Systems Big Data Analytics and Data Science Cyber Security and Network Management Jurnal Nasional Komputasi dan Teknologi Informasi Information System Analysis, Design and Development Digital Multimedia and Visualization Technology
Zainal Arifin Hasibuan
Universitas Komputer Indonesia
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Neuroscience Transportation

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Probity Audit of Procurement Information System: A Case Study of SIMPRODAS Implementation in Bandung Regency Dodi Syaripudin; Lusianto Lusianto; Rizki Adha; Sri Supatmi; Bobi Kurniawan Soegoto; Zainal Arifin Hasibuan
JURNAL SISFOTEK GLOBAL Vol 16, No 2 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.16252

Abstract

Probity audit is an independent assessment activity aimed at ensuring integrity, correctness, and honesty in the public sector procurement process of goods and services. This study analyzes the application of probity audit in the procurement information system (SIMPRODAS – Probity Oversight Information System BEDAS) in Bandung Regency as a form of digital transformation in enhancing accountability and transparency of regional financial management. Using a qualitative research approach through a case study method, this study involves the collection of primary data through in-depth interviews with 15 informants from the Inspectorate, financial management units, and regional work units, as well as secondary data from policy documents and audit reports. The results indicate that the implementation of SIMPRODAS has improved real-time monitoring in the procurement process, but still faces challenges related to human resource competencies, cross-organizational system integration, and clarity of audit normalization guidelines. The study recommends capacity building for audit human resources, refinement of information-technology-based probity audit technical guidelines, and strengthening coordination between the Government Internal Supervisory Apparatus (APIP) and management. These findings provide practical contributions for local governments in optimizing probity audits through the utilization of modern information systems.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Husni Teja Sukmana; Zainal Arifin Hasibuan; Abdul Wahab Abdul Rahman; Luhur Bayuaji; Siti Ummi Masruroh
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.
Co-Authors Abdul Wahab Abdul Rahman Agus Nursikuwagus Andi Agus Salim Andri Heryandi Arvy N. Osma Bella Hardiyana Bobby Kurniawan Bobi Kurniawan S Bobi Kurniawan S. Bobi Kurniawan Soegoto Bobi Kurniawan, Bobi Citra Noviyasari Diana Effendi Dodi Syaripudin Eddy Soeryanto Soegoto Ednawati Rainarli Firdaus, Dony Waluya Hani Irmayanti Hasbu Naim Syaddad Hasbu Naim Syaddad Hidayat Hidayat Husni Teja Sukmana Ifan Dwiguna Irawan Afrianto Irfan Dwiguna Sumitra Luhur Bayuaji Luki Ishwara Lusianto Lusianto Muhamad Nawawi Nanang Abdurahman Petrus Sokibi Ridho Taufiq Subagio Rifqi Fahrudin Rio Yunanto Rizal Rachman Rizki Adha Rizki Adha Siti Ar-Rachmi Ningrum Siti Ummi Masruroh Sri Nurhayati Sri Supadmi Sri Supatmi Sri Titi Handayani Wiyoga Baswardono zulkifli, ridwan