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The Industrial Revolution 4.0 has significantly transformed the education sector, including Islamic education. Digital transformation requires Islamic education to adapt while maintaining Islamic values as the foundation of learning. Islamic education faces challenges such as limited digital competence among educators, unequal technological infrastructure, and curricula that are not yet responsive to digital demands. Therefore, examining Islamic education in this era is essential. This study aims to analyze the role of Islamic education in the Industrial Revolution 4.0, identify challenges and opportunities in digital adaptation, and explore development strategies integrating Islamic values with digital technology. This study employs a literature review method by analyzing relevant national and international journal articles published within the last five years. The findings indicate that Islamic education plays a crucial role in strengthening moral values and character amid rapid technological advancement, while digital integration offers opportunities for adaptive and competitive learning development.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

Nickel has become a strategic mineral in the global industrial value chain, particularly for stainless steel production and electric vehicle battery manufacturing. As one of the world’s largest nickel producers, Indonesia has implemented a downstream industrialization policy aimed at increasing value added and strengthening export performance. This study analyzes the effects of international nickel prices, destination countries’ GDP per capita, exchange rates, and the downstreaming policy on the value of Indonesia’s nickel exports (HS 75) over the period 2010–2023. The study employs a quantitative approach using panel data regression with secondary data covering five major export destination countries, namely China, Japan, South Korea, Thailand, and Singapore. Based on the Chow and Hausman tests, the Fixed Effects Model is selected as the most appropriate estimation technique, indicating the presence of country-specific heterogeneity among importing countries. The results show that destination countries’ GDP per capita and international nickel prices have a positive and statistically significant effect on Indonesia’s nickel export value. The downstreaming policy dummy variable also exhibits a positive and significant impact, suggesting that the nickel ore export ban implemented since 2020 has effectively shifted export composition toward higher value-added processed nickel products. In contrast, exchange rates are found to have no significant effect on export performance. Overall, the findings provide empirical evidence supporting the effectiveness of Indonesia’s downstream industrialization policy and highlight the importance of global demand conditions in driving the performance of processed nickel exports.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

Model driven Development (MDD) has emerged as an efficient software engineering methodology that focuses on using high-level models as primary artifacts throughout the software development process. The methodology involves transforming abstract models into detailed designs, and eventually into executable code, with the assistance of automated tools. This study evaluates the impact of MDD on the Verification and Validation (V&V) processes within secure enterprise software systems. By comparing MDD-based projects with traditional code-centric development approaches, the study highlights the advantages of MDD in reducing verification time, minimizing defect leakage, and improving the traceability of security requirements. MDD significantly enhances V&V efficiency by automating key processes, which allows for earlier error detection and better resource utilization. Additionally, MDD strengthens security compliance by integrating security requirements early in the development lifecycle, ensuring better alignment between system requirements and their implementation. Despite the clear benefits, challenges such as the lack of standardized tools and the need for specialized expertise in model development were also encountered during the study. The findings of this research offer important insights for enterprise software development teams looking to adopt MDD for more efficient and secure V&V processes. Future research should focus on the long-term impact of MDD on security compliance, as well as its adoption across different industries, to fully understand the practical benefits and challenges of implementing MDD in diverse real-world environments.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

The analysis methods used in this study include a case study on the use of closed-loop recycling and an evaluation of biopolymer performance across various industries, both of which are important components in the transformation of the manufacturing industry toward a circular economy. The research findings indicate that recycled materials can reduce carbon emissions by thirty to fifty percent and save production costs by fifteen to twenty-five percent. Artificial intelligence-based sorting technology improves sorting efficiency to 95 percent, and closed-loop recycling maintains the mechanical properties of materials up to 90 percent after four cycles. The degradation rate of biopolymers like PLA and PHA reaches 60-80% within six months, although production costs are still 2-3 times higher. The integrated approach increases resource efficiency by 45% and reduces waste by 60%. To achieve successful implementation, Extended Producer Responsibility (EPR) policies, strategic infrastructure investments, and collaboration from various parties thru the triple helix model must work together.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

The food and beverage (FnB) industry is one of the main supporting sectors of tourism in Indonesia and has experienced rapid growth along with the increasing number of tourist activities and consumer demand. However, previous studies indicate that Service Quality in the FnB industry remains suboptimal, particularly in aspects related to human resources (HR). Issues such as inconsistent service performance, low responsiveness, and limited employee competence and work attitude are commonly identified. This study aims to systematically examine the role of human resources in Service Quality within the FnB industry and to identify key factors, management strategies, and existing research gaps. This research employed a Systematic Literature Review (SLR) method using the PICOC framework, analyzing 20 national and international journal articles published between 2015 - 2025 and retrieved from Google Scholar. The findings reveal that the most influential HR factors affecting Service Quality include competence, communication skills, work attitude, experience, and employee training. Furthermore, effective human resource management practices demonstrate a positive relationship with improved Service Quality. Nevertheless, the review identifies a lack of comprehensive studies integrating HR management and Service Quality within the specific Context of the Indonesian FnB industry, indicating opportunities for future research.

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

This study examines the impact of IT-driven innovation management on IT service effectiveness and competitive value creation within smart organizations. As digital transformation accelerates across industries, organizations are increasingly leveraging advanced IT solutions to enhance service delivery, responsiveness, and customer satisfaction. While traditional IT service management (ITSM) models focus on efficiency and structured processes, the integration of innovation management introduces new opportunities to improve service quality and operational agility. Through a quantitative research design, this study employs regression modeling to assess the relationship between IT-driven innovation management and two key outcomes: IT service effectiveness and competitive value creation. Data were collected from 100 technology-intensive organizations that actively integrate innovation into their IT service management processes. The results demonstrate that IT-driven innovation significantly enhances service quality, customer satisfaction, and organizational competitiveness. Furthermore, a curvilinear relationship was identified, indicating that while moderate innovation leads to improved outcomes, excessive innovation may have diminishing returns. These findings highlight the importance of balancing innovation efforts with business goals to achieve optimal performance. The study also compares innovation-driven IT service management with traditional models, illustrating how innovation fosters agility, responsiveness, and long-term value creation. The implications for smart organizations are clear: integrating innovation into IT service management is essential for maintaining a competitive edge in the rapidly evolving digital landscape. Future research should explore the long-term impact of innovation management on organizational sustainability and growth, considering external factors such as market volatility and technological disruptions.

The integration of Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems has emerged as a critical strategy for modern digital enterprises aiming to enhance customer experience and operational efficiency. This study examines the impact of CRM-ERP integration on customer satisfaction, personalized service, and organizational responsiveness. By adopting a mixed-methods approach, this research combines quantitative customer data analysis and qualitative managerial interviews to assess the benefits and challenges of CRM-ERP integration. Key findings highlight significant improvements in customer experience, with increased satisfaction and personalized interactions facilitated by a unified view of customer data. Operational efficiencies were also realized through streamlined processes, better alignment of departments, and enhanced decision-making based on real-time, data-driven insights. Despite these positive outcomes, challenges such as system integration complexities, data fragmentation, and resistance to change were identified, which hindered the speed of integration and full utilization of the systems. This study demonstrates that CRM-ERP integration provides a competitive advantage by improving both customer service and business agility, particularly in industries undergoing digital transformation. For digital enterprises, integrating these systems is crucial for maintaining a seamless customer experience across various touchpoints and achieving greater operational effectiveness. The paper concludes by suggesting future research on the long-term impact of CRM-ERP integration on customer loyalty, business growth, and the potential role of emerging technologies like AI and blockchain in further enhancing these systems.

Unemployment is a socio-economic problem that can threaten the stability of the Indonesian economy. This study analyzes the effect of minimum wages, exports, foreign investment, and the human development index (HDI) on the unemployment raefrom 1990 to 2023. Using the Ordinary Least Square (OLS) multiple linear regression estimation method, to correct bias in the estimation, the Newey-West HAC standard errors approach is used. Minimum wages and foreign investment have a significant negative effect on the open unemployment rate, confirming that wage increases can boost productivity, foreign investment creates direct jobs through the construction of production facilities and economic multiplier effects in supporting sectors. The most surprising finding of the HDI which has a positive effect and exports which are proven to be insignificant on the unemployment rate, this shows that human capital formation is not in line with existing job opportunities due to rapid technological changes, as well as export-increasing policies which focus more on capital intensity. The study provides important implications for policymakers, maintaining and optimizing minimum wage increases and foreign investment in a measurable manner because they have proven effective in reducing unemployment rates. Reorienting export strategies policy from capital-intensive to labor-intensive, increasing the human development index adjusted to technological developments, especially in the business and industrial world.

The selection of this research topic is based on the important role of packing machines in the noodle production process. As consumer demand continues to increase and industrial competition becomes more intense, optimizing production efficiency is a critical requirement for manufacturing companies. This study focuses on the Tokiwa W500 Packing Machine used at PT. Indofood CBP Sukses Makmur, Noodle Division, Semarang. The research method involves a comprehensive review of the machine control system to evaluate its operational performance. Data collection was conducted through direct observation, structured interviews with machine operators, and relevant literature review. The review emphasizes system performance, operational efficiency, and the level of automation, while identifying potential areas for improvement. The results indicate that the Tokiwa W500 Packing Machine operates in a stable and consistent manner during the noodle packaging process. However, opportunities were identified to enhance the automation system in order to improve production efficiency and reduce the risk of human error. This study is expected to contribute to the development of more effective and optimized control systems for industrial packing machines.

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of peripheral vertigo, characterized by brief episodes of vertigo due to otoconia displacement. Although most previous studies have focused on intrinsic factors such as age, gender, osteoporosis, and metabolic disorders, evidence regarding the role of environmental factors, particularly occupational noise exposure, is limited. Chronic noise has the potential to affect vestibular function through both sensory and vascular mechanisms. This study aims to narratively review the effect of occupational noise exposure on the risk of BPPV by integrating clinical, epidemiological, and experimental findings. The method used is a literature-based narrative review of the PubMed, Scopus, Web of Science, and Google Scholar databases without year restrictions, using the keywords "BPPV", "occupational noise exposure", "vestibular dysfunction", "VEMP", and "otoconia displacement". The search results obtained 25 relevant articles linking BPPV to otolith, hormonal, vascular, lifestyle factors, and occupational noise exposure. The results indicate that chronic noise can cause sensory damage (otoconia and vestibular hair cells), vascular disorders (hypertension, cardiovascular disorders, and inner ear microvascular circulation disorders), and exacerbate lifestyle comorbidities (sedentary lifestyle, osteoporosis, hypertension, diabetes). The discussion confirms that these multifactorial mechanisms explain the susceptibility of industrial workers to BPPV despite normal hearing function. The conclusion of this study is that workplace noise exposure has been shown to play a significant role as a risk factor for BPPV, therefore, prevention strategies, vestibular health monitoring, and healthy lifestyle interventions need to be optimized in occupational health programs.