Publication Search

Education stands as a dynamic force in human and societal development, continually evolving to meet shifting demands and needs. In this modern era marked by rapid transformations, the importance of keeping education abreast with the times cannot be overstated. One effective tool for rejuvenating and advancing education is the SWOT analysis. SWOT analysis, encapsulating Strengths, Weaknesses, Opportunities, and Threats, transcends its traditional business applications to offer profound insights in diverse domains, including education. In the realm of Christian Religious Education (CRE), a critical aspect of the educational landscape, SWOT analysis unveils avenues for both refinement and growth. This paper explores the application of SWOT analysis in the context of CRE, acknowledging its pivotal role in shaping individual character, values, and spiritual beliefs. Tracing the historical trajectory of CRE from ancient churches to contemporary times, the analysis underscores the foundational principles upon which CRE stands. Within the framework of CRE, SWOT analysis serves as a potent tool to discern existing dynamics and chart future trajectories. By identifying internal strengths such as robust faith traditions and biblically grounded curricula, opportunities for enriching CRE education emerge. Simultaneously, weaknesses such as resource constraints and technological integration challenges highlight areas ripe for improvement. Furthermore, SWOT analysis illuminates potential opportunities for CRE advancement, including technological integration and policy shifts. However, it also unveils threats such as societal value shifts and information overload that impede effective CRE delivery. In navigating these dynamics, effective educational management emerges as paramount. Without strategic oversight and management, the pursuit of excellence in CRE remains elusive. Hence, this study delves into the pivotal role of educational management in fostering the quality and efficacy of CRE. Utilizing an analytical methodology, this research delves deep into existing literature and textual sources to unravel insights and generate new knowledge. Through systematic analysis and interpretation, this study seeks to enrich the discourse on CRE and underscore the imperative of strategic planning and management. In conclusion, SWOT analysis emerges as a cornerstone in the enhancement of CRE. By systematically evaluating internal dynamics and external trends, SWOT analysis paves the way for informed decision-making and strategic interventions. Thus, this paper advocates for the systematic integration of SWOT analysis in CRE, heralding a new era of excellence and relevance in religious education.

This research aims to analyze meta data related to the use of the Photomath application. This research uses the Systematic Literature Review (SLR) method. This systematic review identified 11 studies via the Google Scholar database published in 2020-2023. Data collection was carried out by collecting all articles related to the use of the photomath application, with selection criteria and using the PRISMA procedure as a research instrument guide. The research results show that the Photomath application provides a positive, effective and practical learning experience in solving mathematics problems. Training in the use of mathematics applications is considered important to help students understand mathematics learning material.

There has been little research on literature studies related to using the PhotoMath application as a learning medium to improve students' computing abilities. The aim of this research is to analyze whether the PhotoMath application can improve students' computing abilities. The previous research article was taken in 2020-2024. The research method used is Systematic Literature Review (SLR) with the PRISMA protocol for all research articles indexed in Google Scholar and Garuda. The search strategy was adjusted to the selection criteria and involved several moderator variables, namely publication year, journal index, and research material. The data obtained is presented in a quantitative descriptive manner. The results of this SLR research show that learning media using the PhotoMath application can improve students' computing abilities if the application is not misused.

This article is a report study on the development of a learning school management model of Lhokseumawe Sukma National School and the promotion of educational standards with the aim of sustainable educational development. The research focuses on the school profile of Sukma Nation students and the achievements and challenges of the program in its implementation. The study is qualitative and uses survey methods. Information gathered through documents and interviews with relevant authorities, including focus group observations. The study also presents various manifestations of Sukma Bangsa school innovations. The results specifically show that institutional leadership is related to the implementation of five main categories of learning schools, which are personal leadership, shared vision, mental models, situated thinking and group learning. The order of applications according to the system can be seen in the changes in the behavior of the individual regarding information management, information sharing, interpretation and information search. In addition, the student and school management model has become an important part of achieving school effectiveness.

Profit-sharing system in Islamic banks is one of the applications of Sharia since interest contradicts Islamic law. Islamic banks can engage in banking activities like non-Islamic banks as long as they do not contradict Sharia principles. Salam accounting is in the financial statements of PT Bak Syariah Indonesia and to ascertain the conformity of the accounting implementation with PSAK No. 59 and the Fatwa of DSN MUI. aimed at providing an overview of the object based on observable facts and providing an examination of the financial statement application comparison between the research item, PSAK No. 59, and the DSN MUI Fatwa. The findings indicate that PT Bak Syariah Indonesia employs Salam contracts with the following service items in order to execute Sharia accounting for Sharia service products: The application of Sharia accounting for Sharia service goods at PT Bak Syariah Indonesia, as well as Qardh: Haji Guarantee Fund, Export L/C, is in accordance with PSAK No. 59.

This research aims to examine the impact of using the Paylater application on the lifestyle of people who are still not wise in using the paylater application. This research uses a quantitative approach method with the target population, namely the general public who use PayLater services or have used Paylater before. 114 respondents were selected using a questionnaire method using a Google form to conduct an online survey and collect data. The research results show that people rarely use paylater applications in daily use. Because paylater services have negative impacts, such as getting into debt if you use paylater excessively, then impulsive purchases due to easy access to paylater can encourage unplanned impulse purchases and other losses. And unwise use of paylaters can have a negative impact on people's lifestyles and people's finances. This research contributes to the understanding of the negative consequences of paylater for unwise users. This aims to help paylater service providers to increase education and financial literacy for their users, as well as encourage stricter regulations to minimize the potential for paylater abuse.

This study explores the potential of mobile educational applications to enhance student-centered inquiry-based learning (IBL) in secondary school classrooms. As traditional, teacher-centered pedagogies fail to adequately engage students in critical thinking and problem-solving, IBL offers a promising alternative that encourages active participation and deeper learning. The research investigates how mobile applications can support IBL by facilitating the inquiry process, such as data collection, hypothesis formulation, and collaboration. Through a quasi-experimental design involving secondary school students, the study compares the effectiveness of traditional teaching methods and mobile-assisted IBL. Results indicate that students using mobile applications showed significant improvements in critical thinking, engagement, and academic performance compared to those taught through traditional methods. Teachers and students both reported high satisfaction with mobile apps, particularly in terms of ease of use and educational value. The findings suggest that integrating mobile technologies into the classroom can create more interactive, accessible, and personalized learning experiences, fostering critical thinking and enhancing student outcomes. However, challenges related to infrastructure, teacher training, and digital literacy must be addressed to fully harness the potential of mobile-assisted inquiry-based learning.

Energy efficiency has become a cornerstone in industrial optimization, reducing operational costs and contributing to sustainability. This paper reviews key innovative approaches in mechanical systems used to enhance energy efficiency within industrial applications. It covers advances in system design, smart technologies, automation, and predictive maintenance. By understanding these techniques, industries can make strides toward greener production processes, lower energy costs, and reduced environmental impact.

Shopping activities have become a dominant phenomenon in people's lives, one of which is among students. Students are an important segment in society, so they have become a shopping preference that deserves to be understood more deeply. The aim of this research is to analyze student behavior and preferences from shopping experiences. This research uses a descriptive quantitative method with data collection using a questionnaire with closed answers. The questionnaire was filled in by 152 respondents spread across various universities in Indonesia. The research results show that the e-commerce applications that are often used by students are Shopee and Tokopedia. Students prefer to shop through online shops because the prices offered are more affordable than conventional shops. Apart from that, if you shop via an online shop, the activity time is shorter and more efficient. In the online shop, the products offered are quite diverse and varied, and there are lots of promotions on offer so that they attract the attention of buyers. Online shops are used by students to buy fashion and skincare products because the products offered are more diverse compared to offline shops. Based on this data, it can be understood that students choose to shop through online shops using the Shopee and Tokopedia applications and choose to shop for fashion and skincare.

This study aims to determine the implementation of blended learning in improving the scientific literacy of SMA/MA students in Indonesia. This research is a type of meta-analysis research. The sample of this research comes from the analysis of 14 articles that have been published from 2017-2022. The sampled articles have been indexed by SINTA, DOAJ, Google Scholar, Scopus and Copernicus. Research sample search through google scolar and sciencedirect. The sampling technique is purposive sampling technique. The data that can be sampled only has a relationship between the independent variable and the dependent variable, namely blended learning and students' scientific literacy. The data analysis technique in this study is a quantitative data analysis technique with SPSS 21 and JSAP applications with a value of sig.0.005. The application is to calculate the value of Effect Size (ES), Mean and Standard deviation (SD). The results of this study concluded that the application of blended learning was able to increase the scientific literacy of SMA/MA students in Indonesia with an Effect size (ES) of 0.494 and an n-Gain of 0.391. So, teachers in the 4.0 revolution era must be able to apply blended learning models to students so that students are able to face current global competitors.

This research presents a numerical investigation into heat transfer in nanofluid flow using an advanced lattice Boltzmann method (LBM). The study modifies the standard LBM to incorporate the unique properties of nanofluids, such as enhanced thermal conductivity. We simulate convective heat transfer in a pipe with varying nanoparticle concentrations, assessing the effects on heat transfer rates. Results show that nanofluids significantly improve heat transfer efficiency, offering valuable insights for engineering applications in cooling systems.

This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

Artificial Intelligence (AI) is transforming mechanical engineering and industrial processes by introducing unprecedented levels of efficiency, precision, and innovation. From predictive maintenance and autonomous robotics to material optimization and digital twins, AI-enabled systems are reshaping the industry landscape. This article examines key applications of AI in mechanical engineering, exploring how they contribute to sustainable industrial innovation, improve productivity, and pave the way for future advancements.

Industrial robotics has transformed the field of mechanical engineering, enhancing precision, productivity, and safety in various industrial applications. This article examines the key challenges and opportunities that industrial robotics presents within mechanical engineering, alongside an exploration of emerging technologies like AI-enhanced robotics, collaborative robots (cobots), and advanced sensor integration. By addressing the complex issues surrounding robotics and highlighting potential advancements, this paper provides insights into the future of robotics in industrial settings.

This study explores the use of graphene-based nanofluids in enhancing the performance of solar-powered desalination systems. A laboratory-scale desalination system was developed to simulate the evaporation process, powered by solar energy, with the integration of graphene-based nanofluids to improve thermal efficiency. The experimental setup measured evaporation rates, energy consumption, and temperature profiles under varying solar radiation conditions (400–800 W/m²). Results revealed that the system with nanofluids demonstrated up to a 35% increase in evaporation rates compared to the baseline system without nanofluids, indicating enhanced heat transfer properties. Moreover, energy consumption was reduced by up to 20%, highlighting the improved energy efficiency of the system with nanofluids. The system with nanofluids exhibited higher temperatures in the evaporator, confirming more effective thermal utilization. Statistical analyses, including t-tests and regression analysis, confirmed the significant impact of nanofluids on both evaporation rates and energy consumption. This study demonstrates that graphene-based nanofluids offer a sustainable and energy-efficient solution for solar-powered desalination, particularly in areas with abundant solar radiation. The integration of nanofluids not only enhances the efficiency of the desalination process but also reduces operational costs, making it a promising alternative for addressing water scarcity in a sustainable manner. Further research is needed to optimize nanofluid formulations and assess their long-term feasibility for large-scale applications.

This study focuses on optimizing electric vehicle (EV) battery recycling through the use of green chemical processes and circular economy principles. The research aims to enhance the recovery of valuable metals lithium, cobalt, and nickel from used lithium-ion batteries (LIBs) in an environmentally sustainable manner. Green solvents were employed as a safer alternative to conventional, toxic chemicals, minimizing hazardous waste emissions and improving the efficiency of the recycling process. Experimental results showed that the green solvent-based process achieved high recovery rates of 90% for cobalt, 87% for nickel, and 85% for lithium, with metal purity levels exceeding 95% for all three metals. The study also examined the scalability of the green solvent method, revealing its potential to offer more sustainable and cost-effective solutions compared to traditional methods, which typically involve high temperatures and toxic chemicals. Despite the promising results, challenges such as solvent recovery and the adaptation of the process for large-scale industrial applications remain. Nonetheless, the study demonstrates that integrating green solvent-based recycling into the global EV supply chain can significantly reduce environmental impacts, conserve resources, and support the transition to a circular economy. The findings highlight the potential of this recycling method to provide a more sustainable and efficient solution for EV battery recycling, ultimately contributing to the development of a more sustainable EV industry.

The advent of 5G technology has marked a significant shift in the telecommunications industry, offering transformative improvements in service speed, latency, and network reliability. This study explores the impact of 5G on operational efficiency and service innovation in telecom companies. By examining the operational performance of three leading telecom companies that have implemented 5G networks, the research identifies key improvements in speed, cost reduction, and resource optimization. The findings highlight that 5G has enabled companies to achieve up to 100 times faster data transfer speeds compared to previous generations, drastically reducing latency and enhancing network reliability. These improvements contribute to increased customer satisfaction, faster response times, and reduced operational costs. Additionally, the integration of artificial intelligence (AI) for network management has optimized resource allocation and further enhanced the efficiency of telecom operations. The research also demonstrates how 5G has driven innovation in service offerings, such as enabling smart cities, IoT integrations, autonomous vehicles, and real-time patient monitoring in healthcare. While the deployment of 5G offers numerous benefits, the study acknowledges challenges such as high infrastructure costs, digital inequality, and regulatory hurdles. Telecom companies must invest significantly in infrastructure and navigate complex regulatory environments to fully realize the potential of 5G. The study concludes that 5G technology has the potential to reshape the telecom sector, fostering greater competitiveness, service quality, and innovation. Future research should focus on the long-term impact of 5G on customer loyalty, its expanded applications, and its role in advancing future technologies such as 6G.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.