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All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan JSAI (Journal Scientific and Applied Informatics) International Journal of Robotics and Control Systems Jurnal Pengabdian Pada Masyarakat METHABDI Journal of Advanced Health Informatics Research
Iis Setiawan Mangkunegara
Universitas Harapan Bangsa
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Nursing Physics Public Health

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Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation Purwono Purwono; Iis Setiawan Mangkunegara
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1104

Abstract

A brain tumor is a type of disease that is quite dangerous in the world. This disease is one of the main causes of human death and has a high risk of recurrence. There are several types of brain tumor locations such as edema, necrosis to elevation. Segmenting the location of this disease is important to do to support faster recovery efforts. The Convolutional Neural Network (CNN) algorithm, which is part of the deep learning method, can be an alternative to this segmentation effort. The U-Net architecture is part of the CNN algorithm which specifically works on medical image segmentation. This study experimented to build a special U-Net architecture for medical image segmentation that had been optimized with SGD. The data used is BraTS2020O which contains a collection of MRI data. This optimization aims to improve the performance of the U-net architecture for segmenting brain tumor images. The results of the study show that the SGD optimization carried out has succeeded in providing better performance than previous studies. This can be seen from the performance value obtained at 0.9879. This accuracy value indicates an increase in accuracy from previous studies. High accuracy indicates that the SGD-optimized model has good segmentation prediction performance.
Implementasi Arduino Iot Cloud: Potensiometer Sebagai Pengatur Intensitas Cahaya LED Iis Setiawan Mangkunegara; Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 1 (2024): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i1.6083

Abstract

Development of the Internet of Things in this modern technological era plays a significant role in aiding and simplifying tasks across various industrial sectors. One of the open-source electronic devices, such as Arduino, also advances a platform specifically designed for Internet of Things projects called the Arduino IoT Cloud. In this discussion, the author implements the usage of Arduino IoT Cloud by experimenting with a potentiometer to regulate the intensity or brightness of light on an LED through the Wi-Fi module NodeMCU based on ESP8266. The results of this implementation indicate that Arduino IoT Cloud can be operated to receive analog data from the potentiometer and exhibits stable performance in both receiving and reading data from it. These results are expected to serve as a reference for the development of Internet of Things technology by leveraging Arduino IoT Cloud and potentiometers
Bridging the Gap: Implementasi Aplikasi Portofolio OBE untuk Evaluasi Pembelajaran yang Lebih Efektif dan Efisien Imam Ahmad Ashari; Retno Agus Setiawan; Arif Setia Sandi A.; Deny Nugroho Triwibowo; Anggit Wirasto; Iis Setiawan Mangkunegara; Sony Kartika Wibisono
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp50-56

Abstract

The implementation of the Outcome-Based Education (OBE) curriculum is often hindered by repetitive manual administration. This community service aims to facilitate digital transformation through SIMPOBE application assistance for 39 lecturers at Universitas Harapan Bangsa. The method involved a hands-on workshop covering assessment alignment, RPS standardization, and PPEPP-based raw score input. Evaluation results show the application successfully bridged the gap between curriculum data and evaluation reports. Efficiency significantly improved as the system automatically links assessment components to CPL/CPMK, eliminating redundant input. Most participants (scores 4 and 5) expressed readiness for independent implementation by April 2026. In conclusion, portfolio digitalization effectively reduces human error and administrative burdens, supporting accountable institutional academic quality enhancement.
A Narrative Review of Privacy Preserving Artificial Intelligence in Nursing Practice Through Federated Learning Iis Setiawan Mangkunegara; Purwono Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2226

Abstract

The rapid integration of artificial intelligence in nursing practice has enhanced predictive analytics, clinical decision support, and workforce management. However, concerns regarding data privacy, data silo fragmentation, and limited model generalizability remain significant challenges. Federated learning has emerged as a privacy preserving distributed machine learning approach that enables collaborative model development without transferring raw patient data across institutions. This narrative review aims to examine the conceptual foundation of federated learning and analyze its relevance for nursing practice and research. A literature search was conducted using Scopus and ScienceDirect databases covering publications from 2015 to 2025. Articles were analyzed through thematic synthesis focusing on technical architecture, clinical applications, ethical implications, and implementation challenges. The review indicates that federated learning has substantial potential to support predictive risk modeling, multicenter nursing outcome research, and integration within clinical decision support systems while maintaining patient confidentiality. Nevertheless, challenges related to non identical data distribution, governance accountability, interoperability, and digital literacy among nurses must be addressed to ensure safe and equitable implementation. Federated learning represents a strategic pathway for developing collaborative and privacy conscious artificial intelligence in nursing, provided that ethical safeguards, standardized data frameworks, and institutional readiness are systematically strengthened.
Knowledge Distillation in Lightweight U-Net Transformer Architectures for Brain Tumor Segmentation Toat Tuloh; Purwono Purwono; Iis Setiawan Mangkunegara
Journal of Advanced Health Informatics Research Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v4i1.369

Abstract

Brain tumor segmentation from Magnetic Resonance Imaging was an essential step for therapy planning, prognosis evaluation, and treatment monitoring in glioma patients. Manual delineation required substantial time and was prone to inter-observer variability. Although deep learning models achieved high segmentation accuracy, performance improvements were often accompanied by increased computational complexity, limiting their applicability in resource-constrained clinical environments. To address this issue, an efficiency-oriented segmentation framework was developed based on a lightweight three-dimensional U-Net enhanced with a shallow Transformer module and guided by knowledge distillation. The main contribution of this study was the integration of logit-level and feature-level distillation to improve segmentation capability while maintaining low inference complexity. The framework emphasized a balanced trade-off between segmentation accuracy and computational efficiency rather than benchmark maximization. Experiments were conducted using the BraTS 2020 and BraTS 2021 datasets. The official training sets were internally split into eighty percent for training and twenty percent for validation. The student network was trained using a hybrid segmentation loss combined with temperature-scaled logit distillation and bottleneck feature alignment from a higher-capacity teacher model. Model performance was evaluated using Dice score, Intersection over Union, and computational complexity measured in floating-point operations. On the BraTS 2021 dataset, the proposed model achieved Dice scores of 0.6538 for Whole Tumor, 0.5382 for Tumor Core, and 0.5304 for Enhancing Tumor. Per-class Dice values were 0.9706 for background, 0.3028 for necrotic or non-enhancing tumor core, 0.4938 for edema, and 0.4936 for enhancing tumor. The corresponding Intersection over Union values followed similar trends. The model maintained an inference complexity of approximately 54.38 gigafloating-point operations for input patches of size 64 × 64 × 64. These findings indicated that the proposed framework achieved a stable balance between segmentation performance and computational efficiency, supporting practical deployment under limited computational resources
Co-Authors Arif Setia Sandi A. Deny Nugroho Triwibowo Imam Ahmad Ashari, Imam Ahmad Purwono, Purwono Retno Agus Setiawan Sony Kartika Wibisono Toat Tuloh Wirasto, Anggit