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All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Journal of Advanced Health Informatics Research
Agung Budi Prasetio
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Computer Science & IT Control & Systems Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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Comparison of Classification and Regression Model Approaches on the Main Causes of Stroke with Symbolic Regression Feyn Qlattice Purwono, Purwono; Agung Budi Prasetio; Burhanuddin bin Mohd Aboobaider
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.87

Abstract

Stroke is one of the deadliest diseases in the world, caused by damage to brain tissue resulting from a blockage in the cerebrovascular system. Proper treatment is essential to avoid worsening complications in patients. Several main triggering factors for stroke include hypertension, obesity, smoking habits, lack of physical activity, excessive alcohol consumption, diabetes, and high cholesterol levels. The advancement of information technology allows for early disease prediction through the utilization of AI and Machine Learning technology. The vast amount of data available on medical and health services worldwide can be maximized to identify risk factors for various diseases, including stroke. Machine learning techniques can be employed to predict the causes of stroke. In this study, we were inspired to use the Feyn Qlattice model approach to address stroke. Both classification and regression models were tested in this study. The results indicate that the classification model performs better, achieving an accuracy rate of 0.95. In contrast, the regression model yielded less satisfactory results, with R2, MAE, and RMSE values considered inadequate. This conclusion is supported by the regression plot and residual plot, both of which indicate suboptimal performance. Hence, maximizing the use of the Feyn Qlattice regression model in datasets related to the causes of stroke is recommended
Artificial Intelligence in Biomedical Psychology: A Systematic Review of Clinical and Cognitive Applications Wulandari, Annastasya Nabila Elsa; Agung Budi Prasetio; Baballe, Muhammad Ahmad; 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 Baballe, Muhammad Ahmad Burhanuddin bin Mohd Aboobaider Purwono, Purwono Taraknath Paul Wulandari, Annastasya Nabila Elsa Yen-Ching Chang