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All Journal Telematika Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan
Agung Budi Prasetio
Institut Teknologi Tangerang Selatan
Education Health Professions Medicine & Pharmacology Nursing Public Health

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Comparative Analysis of UFW and CSF Using the SEPER Framework Arif Kurniawan; Muhamad Yusuf; Agung Budi Prasetio
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3240

Abstract

This study presents a comparative evaluation of two widely used Linux-based firewall solutions, Uncomplicated Firewall (UFW) and ConfigServer Security & Firewall (CSF), using the SEPER framework, which encompasses Security, Performance, Effectiveness, and Reliability dimensions. While previous studies have examined Linux firewall configurations individually, systematic comparisons that apply a structured evaluation framework such as SEPER remain limited. The experiments were conducted on Ubuntu Server using an intra-host virtualized environment consisting of multiple virtual machines. Network performance was evaluated using throughput and latency measurements, while security effectiveness was assessed through port scanning, SSH brute-force simulations, and mild SYN flood scenarios. System reliability was analyzed based on CPU and memory utilization. The results indicate that UFW and CSF exhibit comparable network performance, with throughput differences remaining below 5%, suggesting no statistically significant performance advantage for either firewall. UFW demonstrates slightly lower resource overhead, whereas CSF provides stronger automated brute-force mitigation through its integrated Login Failure Daemon (LFD), at the cost of modestly higher resource usage. Mild SYN flood tests produced similar outcomes across all configurations, largely influenced by Linux kernel-level mechanisms. Overall, this study highlights a trade-off between resource efficiency and advanced security automation. By applying the SEPER framework, the findings provide balanced and practical guidance for Linux administrators in selecting firewall solutions based on deployment priorities rather than isolated performance metrics.
Artificial Intelligence in Biomedical Psychology: A Systematic Review of Clinical and Cognitive Applications Annastasya Nabila Elsa Wulandari; Agung Budi Prasetio; Muhammad Ahmad Baballe; Taraknath Paul
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.v18i3.2214

Abstract

Biomedical psychology emphasises psychological and neurocognitive assessment through the integration of biological, neurophysiological, and quantitative behavioural data to support clinical decision-making. However, conventional assessment approaches remain limited by issues of objectivity, scalability, and longitudinal monitoring, prompting the utilisation of artificial intelligence (AI) as a computational tool in clinical and cognitive contexts. This systematic review synthesises the application of AI in biomedical psychology with an explicit focus on assessment functions, rather than intervention or therapy, following the PRISMA 2020 guidelines through a systematic search of four major databases. The included studies cover a variety of clinical and cognitive applications with variations in psychological constructs, data modalities, and AI methods. The synthesis results show that AI is most often used for diagnostic classification, risk screening, and continuous estimation of cognitive functions and dimensional constructs. Differences in assessment objectives between clinical and cognitive domains reveal consistent methodological trade-offs related to model selection, validation strategies, and overfitting risks. As a key contribution, this review presents an assessment-oriented cross-domain synthesis and proposes fit-forpurpose design principles as a conceptual framework for developing robust, interpretable, and clinically relevant AI-based assessment systems
Energy Sustainability in Artificial Intelligence for Nursing Practice: Addressing the Hidden Cost Yen-Ching Chang; Agung Budi Prasetio
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2249

Abstract

The adoption of artificial intelligence in nursing practice has accelerated rapidly and offers substantial benefits in terms of efficiency, predictive accuracy, and clinical workflow optimization. Applications such as automated documentation, natural language processing of clinical notes, and decision support systems are increasingly embedded in routine nursing activities. While these technologies enhance performance and productivity, growing evidence indicates that artificial intelligence systems are associated with significant energy consumption during model training, data storage, and operational deployment. The healthcare sector already contributes a measurable proportion of global greenhouse gas emissions, and energy intensive digital infrastructures further amplify this burden. Training advanced artificial intelligence models may generate substantial carbon emissions, and repeated inference processes in daily clinical use accumulate additional energy demand.Despite these concerns, current evaluation frameworks for artificial intelligence in nursing remain primarily centered on clinical effectiveness, usability, safety, and organizational readiness. Energy consumption, carbon footprint, and broader ecological implications are rarely incorporated into technology assessment processes. This omission creates a critical gap between digital innovation and environmental responsibility within nursing informatics. This short communication synthesizes available evidence on the hidden energy costs of artificial intelligence in healthcare and nursing contexts, identifies structural gaps in prevailing evaluation paradigms, and proposes the integration of standardized sustainability metrics. The proposed framework emphasizes explicit reporting of energy consumption, carbon emissions, and life cycle environmental impacts alongside traditional clinical and operational indicators. By reframing artificial intelligence evaluation through a sustainability lens, nursing can contribute to advancing digital transformation that is not only safe and effective but also environmentally responsible
Co-Authors Annastasya Nabila Elsa Wulandari ARIF KURNIAWAN Muhamad Yusuf Muhammad Ahmad Baballe Taraknath Paul Yen-Ching Chang