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Digital transformation in public service delivery encourages government to implement service innovations that are effective, efficient, transparent, and accountable. This transformations is realized through the adoption of innovations capable of responding to public needs in a timely and accurate manner to improve service quality. The Regional Civil Service Agency of East Java Province developed the Rumah ASN Application as a digital-based personnel service innovation to support the needs of civil servants of the East Java Provincial Government and the general public. This study aims to analyze and describe the implementation of the Rumah ASN Application as an innovation in personnel services at the Regional Civil Service Agency of East Java Province. This research employs a qualitative descriptive. The analytical framework is based on the public sector process innovation theory proposed by Khodadad-Saryazdi (2022), which consists of seven key success factors: strategic alignment, governance, leadership, culture, information technology and information system, process actors, and performance evaluation. Data were collected through interviews, observations, and documentation. The findings indicate that the implementation of the Rumah ASN Application has generally been conducted well, but it has not yet reached optimal. Challenges identified for optimizing this service including the needs for continuous user socialization during system updates, optimization of service features for civil servant capacity building, strengthening administrative capacity and cross-sectoral coordination, and the developing the application into a mobile application version.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

Corporate financial performance is an important factor in maintaining business sustainability amid increasingly intense competition. One of the commonly used indicators of financial performance is Return on Assets (ROA), which reflects a company’s ability to generate profits through the efficient use of its assets. Corporate profitability is influenced by various internal factors, including capital structure and liquidity. This study aims to analyze the effect of Debt to Equity Ratio (DER) and Current Ratio (CR) on Return on Assets (ROA). This research employs a quantitative approach using secondary data obtained from corporate financial statements. The research sample consists of 36 observations selected through purposive sampling. Data analysis techniques include descriptive statistical analysis and multiple linear regression analysis using SPSS software. The results show that, partially, the Debt to Equity Ratio does not have a significant effect on Return on Assets, while the Current Ratio has a positive and significant effect on Return on Assets. Simultaneously, Debt to Equity Ratio and Current Ratio have a significant effect on Return on Assets, with Current Ratio being the most dominant variable. The findings indicate that effective liquidity management plays a crucial role in improving corporate profitability. The implications of this study are expected to provide useful insights for corporate management in making financial decisions, particularly related to liquidity management and capital structure.

The development of digital marketing has become a strategic factor in the growth of creative culinary businesses, particularly for micro, small, and medium enterprises facing increasing competition and changes in consumer behavior in the digital era. The use of digital platforms, such as social media and online marketplaces, enables business owners to expand market reach, enhance customer interaction, and optimize promotional strategies more efficiently. Nevertheless, various studies indicate that the implementation of digital marketing has not always produced optimal outcomes due to limitations in digital literacy, human resource capacity, and organizational adaptability. This study aims to systematically examine the role of digital marketing in the development of creative culinary businesses through a Systematic Literature Review approach. The review maps key concepts, research trends, and empirical findings related to digital marketing and organizational learning within the context of creative culinary enterprises. In addition, this study identifies existing research gaps and formulates recommendations for future research issues. The findings are expected to provide theoretical contributions in the form of comprehensive conceptual mapping as well as practical contributions for business practitioners, MSME facilitators, and policymakers in designing adaptive and sustainable digital marketing strategies.

This study explores the integration of adaptive streaming models with edge computing to optimize multimedia delivery, particularly in real-time applications such as video conferencing, live streaming, and virtual reality. The proposed model leverages adaptive compression techniques, including scalable video coding (SVC) and hybrid adaptive compression (HAC), which adjust video quality based on real-time network conditions. The use of edge computing further enhances the model by processing and delivering content closer to the user, reducing latency and optimizing bandwidth usage. The research demonstrates that the edge computing-based adaptive streaming model significantly improves latency by up to 30%, reduces bandwidth consumption, and ensures higher visual quality during video playback, even under fluctuating network conditions. This model addresses key challenges in multimedia streaming, such as maintaining video quality in bandwidth-constrained environments and minimizing buffering times. Furthermore, it enhances the overall Quality of Experience (QoE) for users by providing smoother interactions and real-time responsiveness. The study highlights the potential impact of this model on various fields, including remote education, entertainment, and interactive applications, where low-latency content delivery and high-quality streaming are critical. The findings suggest that integrating AI algorithms for even more efficient compression and expanding edge computing infrastructures will further optimize multimedia streaming in the future, ensuring reliable and high-quality user experiences in increasingly connected environments.

The increasing volume and complexity of data have made traditional 2D visualization methods insufficient for effectively exploring and understanding high-dimensional datasets. Immersive Virtual Reality (VR) presents a promising solution by providing an interactive 3D environment that enhances spatial understanding, task efficiency, and user satisfaction. This research aims to evaluate the user experience (UX) and interaction design quality of immersive VR interfaces for 3D data visualization in complex environments. The study employs a mixed-methods approach, combining usability testing, UX questionnaires, and task-based performance analysis. Participants interacted with VR prototypes designed to visualize complex data and were assessed on their ability to manipulate and explore the data efficiently. The findings show that immersive VR interfaces significantly improve spatial comprehension, reduce cognitive load, and increase task performance efficiency compared to traditional 2D systems. Additionally, user satisfaction was notably high, with participants appreciating the intuitive and engaging interaction methods. The study concludes that immersive VR can provide substantial benefits in real-world data visualization applications, particularly in domains requiring the exploration of complex and high-dimensional data. However, further research is needed to optimize VR interfaces and address challenges such as motion sickness and interaction complexity.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

Reading is a must-have skill for students, regardless of proficiency level. Reading skills are used in all subjects in school. By using reading, they can understand what they learn and obtain information about subjects that help develop their skills. In this term, reading strategies play an important role in defining the success of reading. Many researchers have found that using strategies can help students read effectively and efficiently. This issue is fascinating because success in reading is supported by the strategies readers use. Therefore, the researcher is interested in finding out what reading strategies high school students use. This study aims to examine the reading strategies used by Senior High School students while comprehending descriptive text and their perception of reading strategies. The research design was a qualitative approach based on the theory of case study. The informant consists of tenth-grade students of the eleventh-grade Senior High School. Observation and individual interviews are instruments used by the researcher to collect data, and then the data is analyzed by triangulation, which consists of reducing data, displaying data, and drawing. The results of this study show that there were 7 strategies used by participants namely repeated reading, taking notes, imagine the content, summarizing, bottom up, guessing, getting the purpose of reading, each participant uses more than one strategy in reading, The result also showed that the strategies the students used while reading greatly helped them understand the text and made the reading process more effective and efficient.

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.

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.

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 demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing 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.

Employee performance is a key factor in determining organizational success in achieving its strategic objectives. Various management studies indicate that employee performance is not solely influenced by individual factors, but is also significantly affected by organizational factors, particularly organizational planning and organizational structure. Effective organizational planning provides a clear direction and strategic framework that guides employees in performing their tasks, while an appropriate organizational structure facilitates coordination, clarifies authority, and supports the efficient execution of work. This study aims to analyze the influence of organizational planning and organizational structure on employee performance through a systematic literature review approach. The research method employed is a systematic literature review by examining relevant national and international journal articles published within the last ten years. Data were collected from reputable academic databases to ensure the credibility and relevance of the sources. The selected studies were analyzed qualitatively to identify patterns, relationships, and key findings related to the research variables. The results of the literature review indicate that systematic and well-formulated organizational planning contributes positively to improving goal clarity, work coordination, and employee motivation. Furthermore, an organizational structure that is aligned with organizational strategies plays an important role in clarifying task distribution, authority lines, and overall work effectiveness. The findings also reveal that organizational planning and organizational structure are interrelated and mutually reinforcing in shaping employee performance. This study concludes that the integration of organizational planning and organizational structure is a crucial factor in enhancing sustainable employee performance. The results of this study are expected to provide both theoretical contributions to organizational management studies and practical references for organizations in designing effective management systems.  

The world of education must actively participate in preparing a creative, innovative, and competitive generation in response to advances in technology and science. Schools, as formal educational institutions, have a great responsibility to improve students' entrepreneurial skills by adding innovative product development theory to the learning process. Amidst increasingly fierce competition, product innovation is essential to create added value and maintain the sustainability of managed businesses. Entrepreneurship also plays an important role in driving social change and economic growth. Students are also encouraged to learn to be more creative, take initiative, and dare to take risks if they are given the facilities to develop their entrepreneurial spirit at school. Practical learning, training in innovative product creation, and business management simulations help students understand and apply entrepreneurial principles efficiently. Schools are expected to be a sustainable place for developing students' entrepreneurial character through targeted outreach and instruction. This method not only improves students' knowledge and skills, but also builds independence and innovation, which can help students prepare for the world of work, business, and industry. The goal is to foster an entrepreneurial culture focused on developing personal potential, thereby producing independent, adaptive, highly competitive graduates who are ready to face global challenges.

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 increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.