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This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.

This article investigates recent innovations in industrial engineering and mechanical systems, emphasizing how technological advancements impact manufacturing efficiency, sustainability, and cost-effectiveness. Through a review of current technologies, such as additive manufacturing, automation, and smart materials, the study assesses the key challenges industries face and forecasts emerging trends in mechanical engineering and industrial innovation. The article also discusses the potential for artificial intelligence and machine learning to revolutionize industry standards and drive forward engineering solutions.

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.

This article explores recent advancements in sustainable industrial innovation within the field of mechanical engineering. With the shift towards environmental consciousness, the integration of sustainable practices in manufacturing, energy use, and waste management has become crucial. This paper reviews current trends, emerging technologies, and projected future directions, providing insights into how mechanical engineering can play a pivotal role in fostering sustainable industrial practices.

Air pollution and greenhouse gas emissions produced by motorized vehicles are one of the main problems in big cities. This encourages the development of more energy efficient and environmentally friendly vehicles, such as electric vehicles. This journal discusses the development of energy-efficient electric vehicles for urban transportation, with a focus on battery technology, vehicle design, and charging infrastructure. This research aims to develop energy-efficient electric vehicles that can meet city transportation needs. This research focuses on several aspects, namely: Lightweight and aerodynamic vehicle design: This design can reduce energy consumption and increase vehicle efficiency. Efficient propulsion system: An efficient propulsion system can increase mileage and reduce exhaust emissions. Advanced battery technology: Advanced battery technology can increase energy density and battery life. It is hoped that the results of this research will produce energy-efficient electric vehicles that are environmentally friendly and can help reduce air pollution in big cities.

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.

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.

This research aims to explore the challenges and opportunities of implementing Good Corporate Governance (GCG) in Dapur Bestie, a culinary business, from the stakeholders' perspective. Through a combination of surveys, interviews, and document analysis, we gained insights into the factors influencing the company's GCG practices. The findings indicate that Dapur Bestie faces challenges such as stringent regulations, resource constraints, supply chain complexity, and resistance to change. However, there are significant opportunities to strengthen GCG practices, including enhancing brand reputation, operational efficiency, and stakeholder engagement. The integrity of GCG practices within the organizational culture is identified as key to long-term success. This research suggests strategic steps that Dapur Bestie can take to reinforce their GCG practices, considering the company's vision and the expectations of all involved parties. It is hoped that sustained efforts to improve GCG will yield long-term benefits for the company and society at large.

This study aims to analyze the importance of facilities and infrastructure management in supporting the quality of educational services in educational units. In the context of modern education, facilities and infrastructure not only function as technical support for teaching and learning activities, but also as indicators of the quality of educational institutions. This research uses the library research method by examining various scientific sources, both in the form of journals, books, and relevant research reports. The results of the study show that effective management of facilities and infrastructure includes the process of planning, procurement, utilization, maintenance, and continuous evaluation of educational facilities. Each of these stages has an important role in ensuring the availability of a safe, comfortable, and conducive learning environment for all school residents. In addition, transparent and participatory management can increase a sense of responsibility, care, and efficiency in the use of facilities. The management of facilities and infrastructure based on the needs of students and the development of information technology has proven to be able to strengthen the quality of educational services and support the achievement of learning goals optimally. These findings provide practical implications for schools and policymakers to continue to strengthen data-driven educational facilities management systems and cross-stakeholder collaboration.

This study explores the integration of green practices into nursing protocols as a means of reducing the environmental impact of hospital care. Hospitals significantly contribute to global carbon emissions, and nursing practices are integral to healthcare operations, yet they often lack sustainability considerations. The aim of this research was to develop and implement a set of green-practice guidelines tailored for nursing staff to minimize carbon emissions and improve operational efficiency. Through collaboration with environmental experts, nursing staff, and hospital management, new guidelines were established focusing on waste reduction, energy-efficient practices, and sustainable procurement. A waste-audit assessment was conducted to analyze current waste management practices, while a pilot implementation was carried out in selected hospital wards. The results revealed an 18% reduction in carbon emissions following the integration of these green practices into daily nursing activities. Key contributors to this reduction included waste segregation, the use of reusable medical supplies, and energy conservation measures such as optimizing lighting and HVAC systems. However, challenges such as resistance to change, resource limitations, and the need for specialized training were identified during the pilot phase. Despite these barriers, the research highlights the significant potential for nurses to lead sustainability efforts in healthcare settings. The findings indicate that integrating green practices into nursing protocols can provide environmental, operational, and economic benefits, ultimately contributing to more sustainable healthcare systems. Future research should refine these protocols, explore their broader application, and assess long-term outcomes across various healthcare settings

Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.

The development of cellular communication technology has reached a significant point with the presence of 4G LTE networks, which offer high speed and spectrum efficiency. This study aims to evaluate the performance of 4G LTE networks from two operators, Telkomsel and Axis, at two locations of Sultan Ageng Tirtayasa University (UNTIRTA), namely Campus A and Campus C. The method used is a drive test with the help of the SpeedOf.me site, which measures download speed, upload speed, and latency parameters. The test results show that Telkomsel excels in upload speed with relatively good latency stability, while Axis has the advantage in the highest download speed but shows large fluctuations in performance, especially in the latency parameter. This performance variation indicates that location factors, network load, and device conditions greatly affect service quality. Overall, Telkomsel shows more stable performance, making it more reliable for network-based academic activities, while Axis is suitable for needs with a priority of download speed in optimal conditions. This study is expected to be a reference for the development of network infrastructure in higher education environments.

National education faces challenges related to quality, efficiency and management. Several important problems in the education system include: (1) student moral behavior, (2) distribution of learning, (3) less efficient internal systems, (4) poor organization, (5) education management that is not in line with national development, and (6) lack of professionalism in resources. One effort to overcome this problem is to improve learning strategies, which are very important for the success of the learning process. One of the aspects studied in this journal is the teaching of the Islamic religion from Abuddin Nata's perspective. The results of the literature search show that an Islamic-based learning approach can shape student behavior. One effective strategy is to use an approach that is appropriate to the learning objectives and emphasizes skills and learning processes such as the Islamic education model which integrates skills, problem solving and memory formation.

The Covid-19 virus emerged in late 2019 in Wuhan, China, rapidly spreading to over 190 countries worldwide. Governments implemented various measures, including body temperature checks as an initial step in detecting Covid-19 symptoms. However, manual body temperature measurement using a thermogun has limitations due to direct contact. Therefore, this research develops an automatic, contactless body temperature monitoring system based on Arduino Uno to improve efficiency and reduce physical contact. The system uses an HC-SR04 ultrasonic sensor as a distance limiter for temperature measurement, an MLX-90614 infrared temperature sensor for detecting body temperature, and an Arduino Uno to process data displayed on an LCD in degrees Celsius, with a buzzer as a notification indicator. The test results show that this system achieves a body temperature measurement accuracy of 99.53%, producing results close to the reference device when the object and sensor are within a 1–5 cm range and aligned in a parallel position.

This study examines the effectiveness of bureaucratic reform in achieving good governance at the Investment and One-Stop Integrated Services Agency (DPM-PTSP) of Medan City. The objective is to evaluate how bureaucratic reform impacts performance and transparency in public service delivery. The research method employed is qualitative, using a case study approach, involving indepth interviews and document analysis. The findings reveal that bureaucratic reform has positively influenced the efficiency, accountability, and transparency of DPM-PTSP Medan City. In conclusion, bureaucratic reform is a crucial instrument in achieving good governance in the public sector. 

The purpose of writing this analysis is to investigate the financial performance achievements of Bank Syariah Indonesia (BSI) and Bank Muamalat in the Maqashid Syariah Index, with a focus on profitability, liquidity, asset growth, operational efficiency and sustainable service. The research method used is a literature study, which involves collecting and analyzing data from various relevant and reliable literature sources, including public financial reports, academic books, scientific journals, and other official documents. The research results show that both banks have demonstrated a strong commitment to sharia principles in their operations, with adequate performance in key aspects such as profitability, liquidity, asset growth, operational efficiency and sustainable service. Thus, this analysis provides an in-depth understanding of the contribution of BSI and Bank Muamalat to a sharia-based economy and achieving sharia goals in a broader context.    

Tjiwi Kimia Tbk Paper Factory, the world's leading producer of pulp, paper and paper products, has an important role in the national economy. Financial ratio analysis highlights a company's financial health, focusing on liquidity, solvency, activity, and profitability. The aim of this research is to understand and assess the company's financial health and measure overall financial performance. From this research, it can be seen that ratio analysis shows liquidity fluctuations, a significant decrease in profitability, and a lack of efficient use of assets. Although the company's solvency remains maintained, the main challenge lies in operational efficiency and profit growth in proportion to the increase in assets. In conclusion, a more effective strategy is needed in debt management, increasing operational efficiency, and optimizing asset use to improve overall company performance

In the literature study related to evaluating the efficiency and profitability of Islamic insurance through financial statement analysis, previous studies highlighted various methods and approaches used to measure the performance of Islamic insurance companies. Financial statement analysis is the main focus in identifying factors that affect the operational efficiency and profitability of Islamic insurance companies. Some studies have used traditional financial ratios such as solvency, liquidity, and profitability ratios to evaluate the performance of Islamic insurance. However, due to the unique characteristics of Islamic insurance, studies have also proposed the development of specific metrics that take into account sharia principles, such as sharia compliance ratios and fairness ratios. In addition, non-financial approaches such as technical efficiency and allocative efficiency analysis have also been used to evaluate the operational efficiency of Islamic insurance. This research shows that factors such as scale of operations, business diversification, and managerial efficiency can contribute to the efficiency performance and profitability of Islamic insurance companies. Although there have been many studies conducted in this domain, there is still a need for more in-depth follow-up research to better understand the factors that influence the efficiency and profitability of Islamic insurance. With a better understanding of the performance of Islamic insurance companies, regulators and practitioners can develop more effective strategies to improve the stability and growth of this industry.