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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 7 Documents
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Search results for , issue "Vol. 1 No. 1 (2025): Januari: Systematic Literature Review Journal" : 7 Documents clear
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 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.
Determinan Earnings Response Coefficient (ERC) Ramadhanti, Ella; Mutiara, Intan; Syafadan, Ayu
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.v1i1.202

Abstract

This study examines the Earnings Response Coefficient (ERC) as an indicator of market reaction to earnings announcements, focusing on several determining factors: Corporate Social Responsibility (CSR), financial distress, corporate growth, firm size, and earnings persistence. The primary issue addressed is the inconsistency of previous empirical findings regarding the influence of these factors on ERC, which motivates this study to reassess these relationships through a Systematic Literature Review (SLR) approach. The SLR method was employed to systematically collect, analyze, and synthesize evidence from prior studies to obtain a more comprehensive understanding. The findings reveal that CSR and earnings persistence produce mixed results—some studies show positive, negative, or insignificant effects on ERC—reflecting contextual differences and variations in investor perceptions. Meanwhile, financial distress and firm size generally have a significant positive impact on ERC, suggesting that larger firms and those perceived as financially stable are more likely to receive stronger market reactions. Corporate growth, on the other hand, mostly shows no significant effect. The synthesis highlights that the relationship between these factors and ERC is context-dependent, influenced by company characteristics and the level of information transparency provided to investors. This study concludes by emphasizing the need for a more integrative and contextual approach in analyzing the determinants of ERC and recommends further research to deepen understanding of how market responses to earnings information are shaped by these factors.
The Influence of Inflation, Interest Rates, Capital Structure, and Profitability on Stock Prices in Manufacturing Companies Wulan Ramadhani; Deby Deby; Jesica Dara Tista
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.v1i1.211

Abstract

This study aims to analyze the influence of inflation, interest rates, capital structure, and profitability on stock prices in manufacturing companies. The background of this research highlights the volatility of the Indonesian economy, which is driven by macroeconomic factors that significantly affect capital market performance. Using the Systematic Literature Review (SLR) method, this study synthesized 10 relevant articles published between 2023 and 2025, collected through Google Scholar using specified keywords. The findings reveal varied results: inflation and interest rates generally have a negative influence on stock prices, although some studies report insignificant effects. Similarly, capital structure shows both positive and negative impacts, depending on company conditions and research contexts. Profitability also presents mixed outcomes; some studies found significant relationships, while others reported no influence on stock prices. This literature-based synthesis highlights inconsistencies in previous empirical findings and reinforces the need for further research to clarify the interaction between these variables and stock market performance. The study contributes to providing a comprehensive understanding for investors, financial analysts, and policymakers in making better investment and strategic financial decisions under uncertain economic conditions.

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