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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Terpadu JOURNAL EDUCATIONAL OF NURSING(JEN)
Nasrullah, Muhammad Hudzaifah
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Computer Science & IT Engineering Nursing

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Pemanfaatan IoT untuk Efisiensi Energi pada Pabrik Pintar: Tantangan, Solusi dan Tren Teknologi Suciana, Ewin; Nasrullah, Muhammad Hudzaifah; Christanto, Duta Arief; Cahyadi, Dede; Giantri, Lilik Tiara
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1897

Abstract

This study investigates the role of the Internet of Things in enhancing energy efficiency within smart factories by analyzing current trends, driving factors, and challenges. A Systematic Literature Review with the PRISMA framework was employed to ensure systematic and comprehensive selection of studies. The PICO framework guided the formulation of research questions, facilitating rigorous screening of data sourced from the Scopus database with strict inclusion and exclusion criteria. Findings reveal a substantial increase in IoT-related energy efficiency research in smart factories between 2019 and 2025. Key challenges identified include high sensor energy consumption, communication reliability, and network management complexity. Research limitations stem from the exclusive use of the Scopus database and English-language publications. The study highlights the necessity of interdisciplinary approaches and advanced technologies such as 5G and edge computing to address integration and data security issues, thereby supporting the effective and sustainable deployment of IoT in the manufacturing sector.
Green Technology Adoption: A Systematic Review of Key Trends and Challenges Cahyadi, Dede; Widya, Tilly Raycitra; Christanto, Duta Arief; Nasrullah, Muhammad Hudzaifah; Giantri, Lilik Tiara
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10164

Abstract

This study systematically reviews the trends, drivers, and barriers of green technology adoption, synthesizing insights from 81 articles indexed in the Scopus database from 2019 to 2025. Employing the PRISMA framework and bibliometric analysis, the research aims to provide a comprehensive overview of the academic landscape and offer evidence-based guidance for stakeholders. The findings reveal a growing, albeit limited, academic interest, with a research peak in 2024. Geographically, the discourse is led by developed nations and emerging economies, notably China, while research predominantly focuses on high-impact sectors such as transportation, energy, and manufacturing, leaving critical sectors like agriculture under-examined. Furthermore, this review provides a theoretical contribution by mapping empirical findings onto the Green Innovation Cycle and the Stimulus-Organism-Response (S-O-R) model, thereby strengthening the explanatory power of existing frameworks. We identify key challenges spanning infrastructure, policy, and user behavior, and provide specific recommendations for policymakers, industry leaders, and researchers to foster a more equitable and effective green transition. This research serves as a robust scientific foundation for future studies and strategic initiatives to accelerate global green technology adoption.
Application of Artificial Intelligence in MRI Image Analysis for Radiological Diagnosis: A Systematic Review Nasrullah, Muhammad Hudzaifah
JOURNAL EDUCATIONAL OF NURSING(JEN) Vol 8, No 1 (2025): Journal Educational of Nursing (JEN)
Publisher : Akademi Keperawatan RSPAD Gatot Soebroto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37430/jen.v8i1.241

Abstract

Purpose: This systematic review critically evaluates recent advances in AI applied to MRI image analysis for radiological diagnosis, emphasizing improvements in diagnostic accuracy and clinical utility.Methodology: A systematic literature review (SLR) was conducted using PRISMA guidelines, employing a PICOC framework. A comprehensive search of the Scopus database was performed, and studies were selected based on strict inclusion/exclusion criteria through screening and synthesis.Findings: The review found that AI techniques significantly enhance MRI diagnostic performance (e.g., better tumor detection) and streamline workflows by automating routine tasks. It also notes growing publication trends from 2020–2024 in this field, reflecting increasing global research interest.Research Limitations: The review is limited by its reliance on a single database (Scopus) and a narrow publication window (2020–2024). Many included studies exhibit data biases and lack comprehensive external validation, which may affect generalizability.Practical Implications: These results suggest that AI integration can improve clinical workflows. The authors emphasize the need for standardized protocols and multidisciplinary collaboration to ensure safe and effective implementation of AI in radiological practice.Originality: This study provides an original contribution by systematically synthesizing the latest literature on AI applications in MRI diagnostics, offering a comprehensive overview of current methods and trends. It fills a gap by critically evaluating recent studies and outlining future research directions.
The Influence of Ultra-Processed Food on Childhood Obesity: A Systematic Review Kusmiati, Evie; Sugandi, Erwin Santoso; Lubis, Syahroni; Hayati, Intan Masita; Nasrullah, Muhammad Hudzaifah
JOURNAL EDUCATIONAL OF NURSING(JEN) Vol 8, No 1 (2025): Journal Educational of Nursing (JEN)
Publisher : Akademi Keperawatan RSPAD Gatot Soebroto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37430/jen.v8i1.243

Abstract

Purpose: This study aims to evaluate the impact of ultra-processed food (UPF) consumption on the risk of obesity in children through a systematic review of studies published between 2019 and 2024.Methodology: Using a Systematic Literature Review (SLR) method with the PRISMA approach, this study screened 319 articles from the Scopus database, ultimately selecting 13 relevant articles based on inclusion and exclusion criteria using the PICOC framework.Findings: A significant correlation exists between UPF consumption and childhood obesity risk. UPFs are associated with elevated BMI, increased waist circumference, nutrient deficiency, and addictive eating patterns. Socioeconomic status, educational setting, and advertising exposure exacerbate these adverse outcomes.Research Limitations: The primary constraints encompass methodological discrepancies across the analyzed studies, an absence of longitudinal data, and restricted applicability of findings to developing nations.Practical Implications: These findings endorse the development of evidence-based nutrition policies, including food labeling and UPF advertising restrictions for children.Originality: This research introduces a novel "3P" intervention framework (Product, Place, Promotion) for regulating UPF consumption, incorporating biological and social variables into a holistic analytical model.
Co-Authors Cahyadi, Dede Christanto, Duta Arief Giantri, Lilik Tiara Hayati, Intan Masita Kusmiati, Evie Lubis, Syahroni Suciana, Ewin Sugandi, Erwin Santoso Widya, Tilly Raycitra