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Triwiyanto
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Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya Jl. Pucang Jajar Timur No. 10, Surabaya, Indonesia
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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 312 Documents
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FedBrain-3DMRI: Federated Self-Supervised Learning for 3D Brain Tumor Segmentation using SCAFFOLD Algorithm Chaudhary, Neeshu; Thacker, Chintan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1596

Abstract

Brain tumor segmentation is the most important way to separate tumor areas from healthy brain tissue in medical imaging. This is necessary for making an accurate diagnosis and planning treatment. But building strong deep learning models is often hard because there isn't much labeled medical data available, and strict privacy rules stop data from being shared in one place. Federated Learning (FL) helps keep patient data private by keeping it local, but its performance often drops when data from different hospitals have big differences in quality, imaging protocols, and distribution. Our research seeks to create a privacy-preserving federated learning framework that adeptly manages significant data heterogeneity while ensuring high segmentation accuracy across various institutions. We propose a new two-stage FL framework that allows multiple institutions to work together while keeping their privacy and effectively dealing with complicated non-IID data distributions. To start, we use a Federated Masked Autoencoder (MAE) for self-supervised pre-training. This lets the model learn strong anatomical features from unlabeled MRI scans. Second, the model is carefully fine-tuned using an Attention ResUNet3D architecture to get very accurate tumor segmentation. We use the SCAFFOLD optimization algorithm to keep training stable across all clients, even when the scanner varies from site to site, thereby directly addressing client drift. We also use strategic foreground-biased sampling and Test-Time Augmentation (TTA) techniques to greatly improve segmentation accuracy in difficult, uneven tumor sub-regions. We ran extensive experiments on the BraTS 2024 dataset in simulated federated settings with 10, 50, and 100 different clients. The Dice coefficients we got were 0.826, 0.824, and 0.818, which demonstrate strong performance. In the end, these strong results show that the suggested method works well on a larger scale and can be used in a clinical setting.
Implementation of Real-Time Face Recognition for Secure Weapon Storage Access Control Anisa, Anisa; Mutiara, Giva Andriana; Alfarisi, Muhammad Rizqy
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1608

Abstract

The security of weapon storage warehouses is a critical concern that requires an access control system with exceptionally high reliability, particularly in minimizing false acceptance, where unauthorized individuals are incorrectly granted access. In high-risk facilities, even a single false acceptance incident can lead to serious security consequences. Conventional systems based on physical keys or access cards present limitations, including risks of loss, duplication, and access forgery. Therefore, a biometric-based solution is necessary to enhance identification accuracy and strengthen overall security. This study aims to design and implement a reliable, high-security facial-recognition-based access control system for weapon storage facilities. The proposed system integrates a Multi-task Cascaded Convolutional Neural Network (MTCNN) for face detection, FaceNet for feature extraction, and a Support Vector Machine (SVM) for identity classification. The system is implemented as a standalone application on an edge computing device (mini PC) integrated with an electronic door lock. All detection and decision-making processes are performed locally without reliance on cloud services. System evaluation was conducted under various testing scenarios, including variations in lighting intensity, camera distance, facial attributes, and unregistered face testing. Experimental results show that the system achieved an accuracy of 96.25%. A precision of 100% indicates that no unauthorized access was granted. The recall reached 92.50%, reflecting a small proportion of rejected authorized users. The F1-score of 96.11% demonstrates balanced performance. The False Acceptance Rate was 0%, confirming complete prevention of illegal access. The False Rejection Rate was 7.50%, which remains acceptable in high-risk security environments. The system consistently rejected all unregistered faces and operated in real time with an average door unlocking response time of approximately 1.3 seconds. In conclusion, the proposed system provides reliable recognition performance with a strong emphasis on preventing false acceptance. These findings indicate its suitability for enhancing security in high-risk weapon storage facilities.

Page 32 of 32 | Total Record : 312

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