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Systematic Literature Review Journal
Published by International Forum of Researchers and Lecturers
ISSN : 30895162     EISSN : 3089428X     DOI : https://doi.org/10.70062/slrj.v1i1
Core Subject : Health, Science, Education, Social,
Computer Science & IT Health Professions Nursing Public Health Social Sciences Other
Systematic Literature Review Journal (SLRJ) is an academic journal published by IFREL, focusing on the publication of research findings derived from Systematic Literature Reviews (SLR). This journal provides a platform to showcase research that incorporates SLR methodologies across various disciplines, including computer science, technology, healthcare, education, and social sciences. Mission and Focus SLRJ aims to be a platform that unites diverse studies employing systematic and methodological literature reviews. The journal highlights the importance of objective processes for searching, selecting, and analyzing literature while contributing to identifying research gaps, key trends, and areas requiring further study. Objectives of SLRJ: Provide a platform for researchers to publish findings from systematic literature reviews in their respective fields. Enhance understanding of trends and patterns in existing scientific research. Serve as an essential reference for researchers, academics, and practitioners seeking comprehensive information on specific topics. Topics Covered SLRJ covers a wide range of topics related to SLR methodologies, including but not limited to: Cybersecurity Systems Artificial Intelligence and Machine Learning Information and Technology Management Healthcare and Medical Sciences Education and Curriculum Development Technological Advancements and Innovation Social Sciences and Psychology Submission and Review Process Articles submitted to SLRJ must adhere to high standards in terms of SLR methodology and critical analysis of the existing literature. The journal ensures a transparent and objective review process, guaranteeing the publication of only high-quality research. Each accepted article undergoes a rigorous peer-review process to ensure its validity, quality, and contribution to its respective field.
Arjuna Subject : Umum - Umum
Articles 26 Documents
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Systematic Literature Review on CNN and YOLO Algorithms for Detecting Plant Diseases in Precision Agriculture Dani Sasmoko; Eko Siswanto; Febryantahanuji Febryantahanuji
Systematic Literature Review Journal Vol. 1 No. 1 (2025): Januari: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i1.50

Abstract

Computer vision-based algorithms, especially Convolutional Neural Networks (CNN) and You Only Look Once (YOLO), have become the leading approaches in plant disease detection. CNN excels in extracting complex visual features for disease classification, while YOLO provides high-efficiency real-time object detection capabilities. Both algorithms have shown promising results in various studies, especially with controlled datasets. However, challenges remain in their application in real-world conditions, such as environmental diversity, overlapping symptoms, and poorly annotated data. Future research has the potential to optimize these algorithms through the development of lighter models, the use of transfer learning techniques, and multi-modal data integration. In addition, further exploration of a wider range of diseases, crops, and environmental conditions can expand the application of these algorithms. By leveraging these innovations, computer vision-based plant disease management can be improved to support sustainable precision agriculture.
Systematic Literature Review : Analysis of the Impact of Supply Chain Automation on Worker Welfare in the Sustainable Industrial Sector Toni Wijanarko Adi Putra; Aslina Baharum
Systematic Literature Review Journal Vol. 1 No. 1 (2025): Januari: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i1.51

Abstract

This study aims to explore the impact of supply chain automation on workers’ well-being in the sustainable industrial sector through a Systematic Literature Review (SLR) approach. The transformation driven by Industry 4.0 technologies such as artificial intelligence (AI), Internet of Things (IoT), and blockchain, has presented significant challenges and opportunities. This study identifies the impacts of automation on workers’ economic, social, and psychological aspects, including changes in skills requirements, reduction of manual employment, and its effects on mental health. In addition, this study also highlights the importance of sustainability in automation implementation, by balancing economic efficiency, environmental preservation, and social welfare. By integrating the results of studies from various literatures, this study provides recommendations for policies that support sustainability and social inclusiveness, as well as strategic guidance for industries in adopting supply chain automation sustainably.
Optimizing Blockchain-Based Cybersecurity Systems to Strengthen Resilience Against Ransomware Attacks : A Systematic Literature Review Tanveer Shah; Danang Danang
Systematic Literature Review Journal Vol. 1 No. 1 (2025): Januari: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i1.52

Abstract

This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.
Systematic Literature Review : Use of AI Technology for Management Optimization Information Technology Project Febri Adi Prasetya; Fajar Andi; Noorsidi Aizuddin Mat Noor
Systematic Literature Review Journal Vol. 1 No. 1 (2025): Januari: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i1.137

Abstract

This research is a Systematic Literature Review (SLR) aimed at analyzing the application of Artificial Intelligence (AI) technology in the management of information technology (IT) projects. This study focuses on identifying the AI technologies employed, the benefits gained, and the challenges faced in implementing these technologies. The study gathers and analyzes literature from various leading databases, including Scopus, IEEE Xplore, and SpringerLink, within the timeframe of 2015–2025. The findings reveal that AI technologies such as machine learning, predictive analytics, and natural language processing play a significant role in improving efficiency, reducing risks, and supporting decision-making in IT project management. However, challenges such as data quality, organizational resistance, and implementation costs remain major obstacles in adopting this technology. This review provides comprehensive insights into trends, benefits, and barriers associated with AI utilization, along with recommendations for more effective implementation in the future.
A Systematic Review of Healthcare Information Systems in the Medical Field Balqis Nurmauli Damanik; Syahferi Anwar
Systematic Literature Review Journal Vol. 1 No. 2 (2025): April : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i2.138

Abstract

Healthcare Information Systems (HIS) play a critical role in modern medical services by improving patient data management, clinical decision-making, and overall healthcare efficiency. However, existing HIS models face challenges related to interoperability, security, and processing efficiency. This study conducts a systematic literature review to evaluate cloud-based, blockchain-enhanced, and AI-driven HIS solutions. The proposed hybrid HIS model integrates AI-driven decision-making with blockchain-based security to enhance accuracy, interoperability, and data protection. The evaluation metrics include accuracy, latency, and security score, comparing the proposed system with state-of-the-art HIS implementations. Experimental results indicate that the hybrid HIS model achieves 93.4% accuracy, a security score of 0.90, and reduced latency (220 ms), outperforming traditional cloud-based and standalone blockchain solutions. These findings demonstrate that a hybrid approach balances efficiency and security, making HIS more practical for real-world applications. Future research should focus on real-world deployment, computational cost reduction, and regulatory compliance.
Synergy Between The Merdeka Belajar-Kampus Merdeka (Mbkm) Curriculum And Outcome-Based Education In Realizing An Excellent Graduate Profile Fahrina Yustiasari Liriwati; Zulhimma Zulhimma
Systematic Literature Review Journal Vol. 1 No. 2 (2025): April : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i2.152

Abstract

The implementation of the Merdeka Belajar-Kampus Merdeka (MBKM) curriculum and the Outcome-Based Education (OBE) approach represents a strategic response to the global challenges faced by 21st-century higher education. Both initiatives share the same goal: to produce graduates who are relevant to industry needs and capable of adapting to the times. This article aims to examine the synergy between MBKM policies and the OBE approach in the planning and implementation of higher education curricula to achieve excellent graduate profiles. This study uses a systematic literature review with a descriptive-qualitative approach. The findings indicate that integrating MBKM and OBE encourages universities to design outcome-based curricula with contextual learning rooted in real-world experience. Key success strategies include redesigning outcome-based lesson plans (RPS), cross-sector collaboration, and strengthening assessment systems. This article recommends reinforcing synergy among policymakers, lecturers, and external partners to promote adaptive and sustainable higher education.
Evaluating Hospital Information Systems: A Systematic Review of Effectiveness, Implementation, and Impact on Health Services Administration Surya Utama; Soomal Fatima
Systematic Literature Review Journal Vol. 1 No. 2 (2025): April : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i2.155

Abstract

Hospital Information Systems (HIS), or Sistem Informasi Rumah Sakit (SIRS), play a critical role in enhancing administrative efficiency, decision support, and healthcare service quality. However, their implementation and effectiveness vary significantly across healthcare settings, particularly in low- and middle-income countries (LMICs). This study aims to systematically evaluate the existing literature on HIS effectiveness, implementation barriers, and administrative impact. Using a PRISMA-based Systematic Literature Review (SLR) approach, we examined 14 high-quality studies from multiple scholarly databases including PubMed, Scopus, ScienceDirect, and Garuda. The review applied a hybrid thematic synthesis grounded in HOT-FIT and DeLone & McLean models, combined with a normalized quality scoring system. The findings reveal that HIS implementations positively influence administrative workflow, billing accuracy, and patient throughput, though outcomes are context-dependent. Key challenges include lack of interoperability, resistance to change, and insufficient training. Notably, regulatory mandates and national digital health policies were found to significantly enhance HIS adoption and sustainability. This review contributes a multidimensional synthesis of HIS performance, highlighting the importance of human, organizational, and policy alignment. It offers an evidence-backed framework for HIS evaluation that bridges theory and practice. We conclude that integrated, context-sensitive HIS models are essential for advancing hospital management and public health systems, and recommend further empirical studies on long-term impact and cross-sector integration.
The Role of Homecare Nursing in Strengthening Community-Based Disaster Preparedness and Response: A Systematic Review Hely Hely; Qulliyev Javohirbek G'anijon o'g'li
Systematic Literature Review Journal Vol. 1 No. 2 (2025): April : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i2.157

Abstract

Disaster preparedness and response are critical components of community health resilience, yet the role of homecare nursing within this framework remains underexplored. This study aims to investigate and synthesize existing research on how homecare nurses contribute to disaster management, with a focus on preparedness and response phases. Employing a systematic literature review (SLR) approach based on the PRISMA 2020 methodology, 38 peer-reviewed articles from major databases were analyzed thematically. Key findings reveal that homecare nurses play significant roles in early warning communication, individualized evacuation planning, and continuity of care post-disaster, particularly for vulnerable populations such as the elderly and chronically ill. However, evidence also indicates substantial gaps in training, policy inclusion, and structural integration into national disaster response systems. Thematic synthesis identified three dominant themes: decentralized preparedness, home-based continuity care, and systemic underrecognition of homecare in emergency frameworks. The study concludes that homecare nursing is a strategic yet underutilized asset in disaster risk reduction and recovery efforts. Strengthening policy, competency frameworks, and formal inclusion of homecare in disaster governance are necessary to enhance community resilience globally.
Integrating Psychoneuroimmunology into Wound Healing: A Systematic Review on the Role of Mind-Body Interventions in Nursing Practice Ismayadi Ismayadi; Alisarjuni Padang
Systematic Literature Review Journal Vol. 1 No. 2 (2025): April : Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i2.158

Abstract

This systematic literature review explores the role of psychoneuroimmunology (PNI)-based mind-body interventions in improving wound healing within nursing practice. Wound healing is a complex, multifactorial process influenced not only by cellular and molecular factors but also by psychological and immune responses. Despite the growing body of evidence supporting the efficacy of mind-body practices such as guided imagery, hypnotherapy, and meditation in managing stress and modulating immune responses, there is limited integration of these interventions into clinical nursing practices. This review aims to bridge this gap by synthesizing studies published between 2020 and 2024 that examine the impact of these interventions on wound healing outcomes. The review follows the PRISMA protocol, analyzing data from 50 primary studies focusing on RCTs, systematic reviews, and quasi-experimental designs. The results show significant improvements in wound closure rates, pain reduction, and immune modulation (e.g., reduction in cortisol and pro-inflammatory cytokines) in patients who received mind-body interventions. The findings support the hypothesis that mind-body interventions, by addressing both psychological stress and immune function, enhance wound healing. The proposed framework for integrating PNI-based interventions into nursing practice could improve patient outcomes in chronic wound management. Future research should focus on long-term studies with larger sample sizes and standardized intervention protocols to further validate these findings.
A Systematic Literature Review: Factors Affecting Earnings Management Anggun Dwi Lestari; Intan Putri Suryati; Warti Asih Febriyanti; Putri Maharani
Systematic Literature Review Journal Vol. 1 No. 1 (2025): Januari: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i1.200

Abstract

Earnings management is a form of deviation in the process of preparing financial statements, namely affecting the level of profit displayed in the financial statements. This study aims to identify and analyze the factors that influence earnings management, namely Solvency, Corporate Social Responsibility , Profitability, and Company Size based on the findings of previous studies . The method used is the Systematic Literature Review (SLR). Data collection related to similar research was obtained from 50 journals from the Google Scholar database with a publication year range of 2023-2025. The results of this study indicate that the factors that have a significant effect on earnings management, namely Solvency, have an effect on earnings management. The greater the level of this solvency ratio , the greater the opportunity for managers to carry out earnings management so that the company can more easily obtain funds from creditors. Corporate Social Responsibility influence on earnings management. Improving sustainability performance through CSR by companies can encourage management to take higher earnings manipulation actions. Profitability influences earnings management. When the higher the profits earned by the company, the higher the Earnings Management practices carried out by the managers. Company ​size influences earnings management. The larger the size of a company, the smaller the opportunity to carry out earnings management.

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