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Radinka Journal of Science and Systematic Literature Review
Published by Radinka Jaya Utama Publisher
ISSN : -     EISSN : 29885825     DOI : https://doi.org/10.56778/rjslr
Core Subject : Health, Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Public Health
The Radinka Journal of Science and Systematic Literature Review (RJSLR) is a journal that focuses on research articles, reviews, and empirical research. This journal aims to facilitate research in all areas of life until the research articles and SLR method have been applied. The Radinka Journal of Science and Systematic Literature Review (RJSLR) accepts manuscripts in the fields of natural sciences, health sciences, medicine, agriculture, computing, tourism, management, law, and architecture. RJSLR publishes high-quality scientific research results: research articles, systematic literature reviews, structured literature reviews, meta-analyses, and bibliographic analyses
Arjuna Subject : Ilmu Pertanian dan Biologi (Semua) - Ilmu Pertanian dan Biologi (Lain-Lain)
Articles 80 Documents
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A Comprehensive Review on Microbial Bioremediation of Industrial Dye Dey, Sreedeep; Mandal, Barnali
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 2 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i2.518

Abstract

Synthetic dyes, widely utilized across industries such as textiles, cosmetics, and pharmaceuticals, represent a significant environmental hazard due to their persistence, toxicity, and resistance to conventional treatment methods. Current remediation strategies, including coagulation, adsorption, and advanced oxidation processes, are limited by high energy demands, secondary pollutant generation, and inefficiency in degrading recalcitrant dye structures. This review comprehensively evaluates microbial dye remediation as a sustainable alternative, emphasizing the enzymatic potential of bacteria, fungi, algae, and yeasts. Critical findings underscore the efficacy of microbial enzymes, including laccases, azoreductases, and peroxidases, in degrading complex dyes such as azo and anthraquinone derivatives into less toxic or mineralized products. Microbial consortia demonstrate enhanced degradation through metabolic complementarity, while innovative bioreactor systems, such as microbial fuel cells and membrane bioreactors, achieve improved efficiency and energy recovery. Despite these advances, challenges such as the production of toxic intermediates (e.g., aromatic amines), microbial sensitivity to environmental fluctuations, and sludge generation remain obstacles to industrial-scale application. The review highlights the potential of integrating microbial systems with nanotechnology and advanced oxidation processes to address these limitations. Genetic engineering and synthetic biology are proposed as critical tools for enhancing microbial resilience and enzymatic specificity. Future research should focus on hybrid remediation approaches, real-time environmental monitoring, and the development of standardized, scalable protocols. By synthesizing advancements in microbial biotechnology and wastewater management, this review provides a strategic framework for addressing industrial dye pollution, aligning with global sustainability goals and advancing the field of bioremediation.
Analysis and Mathematical Application of Taylor Series to Crystallization of Rock forming minerals from Magma Ifeanyi, Achuenu; Lekmang C. Isah; Hyeladi Dibal; Geoffrey Mica Kumleng
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 2 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i2.553

Abstract

Power series in magma is the power of a mineral to form from magma at a controlled condition of temperature or pressure and the process of mineral formation from magma using power series is called Taylor series in magma. The reactions in magma are very complex because of several anionic and cationic substitutions of different sizes and charges in the magma and in this case, complex problems require complex solutions. This research was focused more on using Bowen and Goldschmidt concepts concerning the elemental substitution and distribution of chemical elements in rocks throughout the time of crystallization with mathematical foundations such as Taylor series to predict major minerals, encompassing olivine, pyroxene, amphibole, mica, and feldspar, and their associated rocks, encompassing granite, basalt, andesite, and trachyte. Findings have shown that, in a mathematical context, Bowen’s and Goldschmidt rules were mathematically connected using the Taylor series.
Spatiotemporal Water Quality Modeling Using Deep Learning Architectures Oise, Godfrey; Ejenarhome Otega Prosper; Oyedotun Samuel ABIODUN
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.519

Abstract

Water quality monitoring is vital for ensuring public health, environmental sustainability, and economic resilience. Traditional monitoring techniques, while precise, often fall short due to high costs, labor intensity, and limited temporal and spatial resolution barriers that are increasingly critical amid accelerating urbanization, climate change, and pollution. This study explores the application of deep learning architectures to spatiotemporal water quality modeling, leveraging diverse datasets comprising historical records, sensor readings, and government sources. Supervised learning techniques were evaluated for predictive and classification tasks, including Support Vector Regression (SVR), Random Forests, XGBoost, and Decision Trees. SVR yielded strong regression performance for Water Quality Index (WQI) prediction with an R² of 0.9693 and low mean squared error, while XGBoost and Decision Trees demonstrated robust classification accuracy above 94%, with Decision Trees excelling in macro-averaged metrics. Unsupervised learning using DBSCAN revealed moderate clustering potential, but also emphasized the limitations of density-based approaches for noisy environmental data. Exploratory analyses offered insights into parameter distributions and interdependencies, including Kernel Density Estimation, correlation heatmaps, box plots, PCA, and t-SNE. While the study confirms the potential of AI in water quality monitoring, it also underscores challenges such as data imbalance, limited minority class precision, and the need for interpretable and scalable models. Future work should integrate explainable AI, edge computing, and hybrid domain-informed models to foster real-time, equitable, and sustainable water monitoring solutions aligned with SDG 6. This research demonstrates the promise of deep learning in transitioning water quality management from reactive to predictive paradigms.
Utilizing Deep Learning to Influence Design Decisions and Predict Future Scenarios Oise, Godfrey; Ejenarhome Otega Prospera; Oyedotun Samuel ABIODUN
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.523

Abstract

Deep learning is increasingly transforming design practice by enabling data-driven decision-making, predictive analysis, and the automation of visually complex tasks. This study investigates the application of a Convolutional Neural Network (CNN) to the CIFAR-10 dataset to demonstrate how image-based deep learning models can support design analysis and future-scenario prediction across fields such as architecture, product development, and urban planning. The model was developed using a sequential CNN architecture with convolutional, pooling, batch normalization, and dropout layers and trained over 20 epochs using the Adam optimizer. Performance evaluation employed accuracy, precision, recall, F1-scores, confusion matrices, and ROC–AUC curves to provide a transparent and interpretable assessment of model behavior. The CNN had a training accuracy of 89% and a test accuracy of 77%. Its macro-averaged precision, recall, and F1-scores were 78.8%, 79.0%, and 77.5%, respectively. Results show strong discriminative capability but also highlight misclassification challenges among visually similar classes and signs of overfitting. These findings emphasize both the potential and limitations of deep learning when applied to design workflows. The study concludes that CNN-based visual analysis can meaningfully inform design decisions, identify hidden patterns, and support predictive scenario modeling, underscoring the need for interpretability and responsible AI integration in design disciplines.
Review on Biodiesel Production from Sandbox (Hura crepitans) Seed Oil Used in Compression Ignition Engine Adamu, Yakubu; Abubakar Ali Fachway; mohammed Hadi Ibrahim; Nasiru Yunusa; Nafiu Ishaq Adamu
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.578

Abstract

The rising concern over the exhaust emissions yielded by the increased demand for diesel as well as the realization that diesel is non-renewable has triggered various iniatiatives to search for more environmentally friendly and sustainable alternatives. The goal of this study is to review the processes involved in the extraction of biodiesel from sandbox (Hura Crepitans) seed oils and analyze the performance of the engine operating under diesel engine thermodynamic conditions. This review describes the sandbox oil and biodiesel yield and sandbox oil performance with biodiesel blends in operating diesel engines. Literature provided data contrasting conventional diesel with diesel-biodiesel blends that showed the diesel-biodiesel blend had lower ignition delay coupled with a lower heat release rate and slightly better efficiency
A reimposition of the death penalty in South Africa maphosa, motlatso
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.584

Abstract

Debates surrounding capital punishment continue to influence criminal justice policy and human rights discourse globally. In South Africa, the Constitutional Court’s landmark judgment in S v. The Death Penalty declared section 277(1)(a) of the Criminal Procedure Act unconstitutional, primarily on the basis of the rights to life, dignity, and equality. Despite the ruling and persistent high levels of violent crime, including sexual offenses and murder, there has been a renewed public and political interest in the possible reinstatement of capital punishment. This paper will undertake a critical and evidence-based examination of this debate within South Africa’s constitutional and criminological context. The analysis focuses on three core concerns that shape the contemporary discourse: the potential for wrongful convictions and the irreversible consequences of execution; the influence of bias, discrimination, and systemic inequality on sentencing outcomes; and international perspectives, particularly the normative positions of the European Union and the United Nations, which consider the death penalty incompatible with evolving human rights standards. Drawing on comparative research, crime data, and international legal developments, the paper assesses whether the reintroduction of the death penalty could have any demonstrable effect on reducing serious violent crime in South Africa. The central aim is to evaluate the criminological claim that capital punishment functions as a deterrent in cases of sexual offenses and murder while critically reflecting on whether such a measure could enhance societal safety within a constitutional democracy. Ultimately, the paper interrogates the tension between demands for harsher punitive measures and South Africa’s commitment to human rights, proportionality, and evidence-based criminal justice policy.
Effect of Supply Chain Integration on Organization Performance:A Mediating Role of Competitive Advantage. An Evidence from Manufacturing Firms in Ethiopia. Megersa, Gezew; Mesfin LEGESSE
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.585

Abstract

This study aimed to fill a knowledge gap in the literature by examining the effect of SCI on organizational performance with competitive advantage. It used a mixed approach and an explanatory and descriptive research design. The study collected data from both primary and secondary sources and used probability sampling, specifically simple random sampling, to examine the relationship between SCI ingredients and organizational performance. The survey was conducted using self-administered and structured, closed-ended questionnaires that were distributed to respondents. Amos in SPSS was used to evaluate the data. While SEM (structural equation model) analysis was used to characterize the causal influence between and among the dependent and independent variables under inquiry, correlation analysis was used to determine the direction and intensity of the association between variables. According to the study, all SCI components and organizational performance have a positive and statistically significant relationship. Regarding SEM analysis, the study discovered that SCI had a favorable and noteworthy impact on organizational performance and competitive advantage. The study concluded that enhanced organizational performance is a result of the implementation of SCI and competitive advantage. It is advised that businesses consistently apply SCI correctly to enhance performance and obtain a competitive edge.
The Role of Computer Simulation Impact for Sustainable Development in Engineering Experiments: Mini Review Hosan, Shen; Perera, Hasith; vithanage, Vimukthi; Wijesekara, Dasith; Kelum, Anjula; koswattage, kaveenga
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.590

Abstract

Sustainable development has become a foundational principle in modern engineering research and practice. Conventional physical experiments frequently require considerable resource utilization, produce waste, and demand substantial time, thereby presenting obstacles to environmental objectives. This paper aims to evaluate the impact of computer-based simulations as a transformative approach to engineering experiments, specifically examining how they align with sustainability goals compared to conventional methods. This study employs a mini-review methodology, synthesizing data from various case studies across engineering disciplines. The analysis focuses on three primary sustainability metrics: resource efficiency, environmental impact reduction, and temporal optimization. The findings demonstrate that computer simulations drastically reduce the carbon footprint of research by minimizing the need for physical prototypes and hazardous materials. Furthermore, simulations allow for rapid iterative testing, which fosters innovation while ensuring high experimental rigor. Case studies show that integrating simulation tools can lead to a significant decrease in material waste up to 60-80% in certain manufacturing and structural testing scenarios. Conclusion: This study concludes that computer-based simulations are not merely a technical convenience but a critical driver for sustainable development in engineering. By maintaining experimental scrupulousness while enhancing efficiency, simulations provide a viable pathway for future-proof engineering practices that balance technological progress with environmental responsibility.
Advance Pumping Technologies for Sustainable Bio Energy Systems: A Review of Efficiency Metrics and Integration Strategies Perera, Hasith; hosan, shen; wijesekara, dasith; vithanage, vimukthi; sbeysinghe, shakya; koswattage, kaveenga
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.595

Abstract

The paper presents a systematic review of the pump technologies in industrial sectors by comparing the data on operational performance and case studies. Three types of pumps were considered, namely, positive displacement (rotary, reciprocating, linear), kinetic (axial flow, centrifugal, submersible), and special (vacuum, jet, peristaltic). The approach included literature review on technical specifications, evaluation of literature on the field performance of the pumps in industrial functions, and comparative evaluation of the pump selection criteria in the industries such as oil and gas, chemical processing, pharmaceuticals, mining, and bioenergy. It has been shown that positive displacement pumps are effective in high-viscosity flows more than 10,000 cP, and screw pumps are the most efficient with 80-95% efficiency covering the widest viscosity (1 to 10¹⁰ cP). The pressure capacity of reciprocating systems was excellent and plunger pumps were made to a pressure of up to 1,000 bar with low leakage rates of 0.1-0.5%. The most effective pumps were kinetic pumps that were suitable in high flow applications and in the case of axial flow systems that operated on a volume of up to 80,000 L/min. Positive displacement pumps were necessary in the bioenergy processes to transport biomass and digestate and centrifugal pumps to manage biogas condensate in the best way. This analysis results in the development of the evidence-based selection framework that allows operational cost savings of 15-30% due to the increase in the efficiency to the same extent and the achievement of completion of bioenergy process efficiency by 12%. These results give practical recommendations on how to maximize the choice of fluid transport systems in various industrial uses.
Artificial Intelligence and the Transformation of Media: Rethinking Journalism and Communication in the Algorithmic Age Najar, Ph. D, Mehrajudin Aslam
RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW Vol. 3 No. 3 (2025): Radinka Journal of Science and Systematic Literature Review
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/rjslr.v3i3.655

Abstract

Artificial Intelligence (AI) has become a defining technological and cultural force in the media and communication landscape. Its integration into news production, content curation, and audience analytics has reconfigured journalistic practices, redefined professional ethics, and reshaped public discourse. Through automation, personalization, and verification systems, AI technologies now influence both how media is created and how it is understood. This conceptual paper examines AI’s multifaceted role in the transformation of media through three theoretical lenses: media ecology, technological determinism, and critical algorithm studies. It develops an analytical model, the Automation–Personalization–Verification (APV) Framework, to explain the systemic influence of AI on production, distribution, and credibility processes within journalism and digital communication. The paper argues that while AI enhances efficiency and creativity, it simultaneously generates new ethical and epistemological dilemmas concerning transparency, bias, and trust. It concludes by emphasizing the need for human-centered AI that balances technological innovation with journalistic integrity and public accountability.

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