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Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan
Published by Universitas Harapan Bangsa Purwokerto
ISSN : 19791034     EISSN : 26561034     DOI : -
Core Subject : Health,
Health Professions Medicine & Pharmacology Nursing Public Health
Viva Medika Is a journal that publishes articles or research results relating to health, nursing and midwifery issues. Viva Medika is published by Harapan Bangsa University twice a year (September and February). The mission of the Journal of Viva Medika is to disseminate and discuss scientific writings on midwifery, nursing, and various issues within the scope of health problems. This journal is intended as a medium of communication for lecturers and people who have attention to health, obstetrics, nursing.
Arjuna Subject : -
Articles 5 Documents
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Search results for , issue "Vol 18 No 3 (2025)" : 5 Documents clear
Formulation and Physical Evaluation of Depilatory Cream from with Variation Concentrations of Turmeric (Curcuma longa) and Bromelain Enzyme Ratnasari, Diah; Na’imah, Janatun; Trikurniadewi, Nastiti; Agustin, Irani; Prastiyo, Danu
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.v18i3.2064

Abstract

The Indonesian cosmetics industry market is predicted to grow by 5.91% per year, including skin care products. One type of skin care is removing unwanted hair from the skin because this hair can interfere with appearance, especially for women. One way to remove this hair is by using hair removal cream (depilatory cream). One natural ingredient that has depilatory properties is turmeric. Depilatory cream also requires sulfhydryl protease enzymes that can break down the molecular structure of proteins contained in hair into amino acids. One protease enzyme is bromelain. The purpose of this study was to obtain the best formula for depilatory cream with varying levels of turmeric and bromelain enzymes based on physical evaluation results. The research method used was experimental research with stages of turmeric extraction, bromelain enzyme isolation, depilatory cream formulation, and physical evaluation of the depilatory cream. The results showed that Formula 1 was the best turmeric-based depilatory cream formula with bromelain enzyme based on physical evaluation results.
Association Between Maternal Myopia and Myopia in Children Aged 10-12 Years in Karanggondang, Indonesia: A Cross-Sectional Study Nabila Dwi Desi Puspita; Susanto, Herry; Indra Tri Astuti; Loan Thi Dang
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.v18i3.2197

Abstract

Myopia is the most common refractive error among school-aged children and a growing public health concern, particularly in developing countries. Although parental myopia is widely recognized as a genetic risk factor, evidence from Indonesia, especially among children aged 10 to 12 years, remains limited and inconsistent. By focusing on maternal myopia as a single and practical familial indicator in school-based settings, this study also considers a null association as a meaningful context-specific finding. This observational analytic study employed a cross-sectional design and was conducted from August to October 2025 in three public elementary schools in Karanggondang, Indonesia. A total of 88 child-mother pairs were recruited using purposive sampling. Visual acuity in both children and mothers was assessed using a Snellen chart without cycloplegic refraction. Refractive status was classified dichotomously as myopia or non-myopia. Data were analyzed using descriptive statistics and Chi-square tests with Yates’ continuity correction, and associations were expressed as odds ratios with 95 percent confidence intervals. The prevalence of myopia was 20.5 percent among children and 59.1 percent among mothers. Childhood myopia was slightly more frequent among children of myopic mothers at 12.5 percent compared with 8.0 percent among those of non-myopic mothers. However, no statistically significant association was found between maternal myopia and childhood myopia, with a p value of 1.000. The estimated odds ratio was 1.12 with a 95 percent confidence interval of 0.385 to 3.209. Maternal myopia was not a significant independent predictor of myopia in children aged 10 to 12 years in this population. This context-specific null finding suggests that maternal myopia alone may have limited explanatory value. Preventive strategies should prioritize modifiable lifestyle factors alongside routine vision screening, and future studies should incorporate cycloplegic refraction and broader familial and environmental measures
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
Understanding the Role of Artificial Intelligence in Community and Home Nursing Care: A Systematic Literature Review Sony Kartika Wibisono; Oktavia Putri Handayani; Burhanuddin bin Mohd Aboobaider
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.v18i3.2225

Abstract

Community and home nursing care are increasingly central to health systems in response to population ageing, rising chronic disease burden, and the need to reduce avoidable hospital utilization. Artificial intelligence (AI) has emerged as a technological innovation with potential to support nursing practice in non-hospital settings. However, the role and implications of AI within community and home nursing care have not been systematically synthesized. This systematic literature review aimed to examine how AI supports community and home nursing practice, identify the types of AI technologies applied, and analyze their reported outcomes and implications for nursing care. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 237 records were identified through electronic database searches. After duplicate removal and screening, 38 full-text articles were assessed for eligibility, and 15 studies were included in the final qualitative synthesis. The included studies, published between 2024 and 2026, encompassed diverse methodological designs and were conducted in community-based, home health, telemonitoring, and mobile nursing contexts. The findings indicate that AI technologies primarily include machine learning–based predictive models, clinical decision support systems, telemonitoring platforms, digital wound assessment tools, and large language model–supported analytics are used to enhance risk prediction, remote monitoring, chronic disease management, and care coordination. Across studies, AI was associated with improved early detection of clinical deterioration, enhanced workflow efficiency, and potential reductions in hospital admissions. Nevertheless, effective implementation depended on nurse engagement, system usability, digital literacy, and organizational support. AI demonstrates substantial potential to strengthen community and home nursing care when integrated within a human-centered and ethically grounded framework that preserves professional nursing judgment.
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.

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