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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.

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 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.

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.

The influence of self-efficacy, work engagement, and financial compensation is crucial to determine the extent of their impact on employee performance at PT Sinergi Gula Nusantara PG Meritjan. By understanding these relationships, company management can assess how these factors affect employee performance and thereby formulate more targeted human resource planning and development strategies in the future. This study employed a saturated sampling technique, in which the sample consisted of all permanent employees of PT Sinergi Gula Nusantara PG Meritjan, totaling 77 respondents. Data were collected through questionnaires, observations, and interviews. The results of the analysis indicate that self-efficacy, work engagement, and financial compensation have a significant effect on employee performance, both partially and simultaneously. This is evidenced by the multiple linear regression analysis, which shows significance values below 0.05 and a coefficient of determination of 0.677 or 67.7%, indicating that self-efficacy, work engagement, and financial compensation explain 67.7% of the variance in employee performance.

The rapid development of globalization and the acceleration of digital transformation have encouraged organizations to adopt more adaptive and collaborative work practices. In this context, collaborative culture has become a strategic element that plays a crucial role in enhancing organizational effectiveness and competitiveness. This study aims to comprehensively examine the concept, characteristics, forming factors, and theoretical foundations of collaborative culture in modern organizations. The research employs a qualitative approach through a literature review, analyzing reputable national and international journal articles, textbooks, and relevant institutional reports. Data analysis is conducted using a descriptive-analytical technique by synthesizing findings from previous studies. The results indicate that collaborative culture significantly contributes to improved communication quality, work coordination, adaptability, and both individual and organizational performance. Collaborative culture is shaped through the integration of shared vision, open communication, trust, willingness to share resources, collaborative leadership, flexible organizational structures, and the support of collaborative technologies. This study also highlights that the success of digital transformation largely depends on the strength of an effectively internalized collaborative culture. The findings are expected to provide a theoretical reference for organizations and researchers in developing sustainable collaborative culture strategies.

The banking sector plays a strategic role in supporting financial system stability and capital market development. Market performance, reflected through stock returns, represents investor confidence in a firm’s prospects and sustainability. In recent years, investors have increasingly considered non-financial factors such as intellectual capital and corporate social responsibility in evaluating firm value. However, empirical findings regarding the effect of these factors on market performance remain inconsistent, particularly in the Indonesian banking sector. This study aims to examine the effect of intellectual capital and corporate social responsibility on market performance of conventional commercial banks listed on the Indonesia Stock Exchange during the 2021–2024 period. This research employs a quantitative approach using secondary data obtained from annual reports and sustainability reports. Intellectual capital is measured using the Value Added Intellectual Coefficient method, while corporate social responsibility is measured using a disclosure index based on the Global Reporting Initiative. Market performance is proxied by stock returns. Data analysis is conducted using multiple linear regression with the Ordinary Least Squares approach. The results indicate that intellectual capital and corporate social responsibility have a positive and significant effect on market performance. These findings suggest that effective management of intangible assets and social responsibility disclosure can enhance investor perception and firm value. The results provide important implications for bank management in formulating value-enhancing strategies and for investors in making investment decisions.  

The advancement of automotive technology has accelerated the adoption of renewable‑energy‑based electric vehicles, including the integration of solar panels on electric scooters. Indonesia’s tropical climate provides abundant solar energy potential; however, the limited surface area of scooters often restricts panel placement to the footrest section. This study aims to evaluate the impact of using a 10 mm clear acrylic cover on the performance of a 50 Wp monocrystalline solar panel in an electric scooter battery‑charging system. An experimental method was employed by comparing the panel’s performance under two conditions: without a cover and with the acrylic cover installed. Key parameters observed included voltage, current, and charging power, recorded using a data logger. Tests were conducted for 30 minutes under varying solar radiation intensities. The results indicate that the acrylic cover reduces the panel’s output power, from 55 W to 45 W at a solar radiation intensity of approximately 1100 W/m². These findings suggest that the use of an acrylic cover must be carefully considered to maintain optimal charging system performance.

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.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

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 advancement of automotive technology has accelerated the adoption of renewable‑energy‑based electric vehicles, including the integration of solar panels on electric scooters. Indonesia’s tropical climate provides abundant solar energy potential; however, the limited surface area of scooters often restricts panel placement to the footrest section. This study aims to evaluate the impact of using a 10 mm clear acrylic cover on the performance of a 50 Wp monocrystalline solar panel in an electric scooter battery‑charging system. An experimental method was employed by comparing the panel’s performance under two conditions: without a cover and with the acrylic cover installed. Key parameters observed included voltage, current, and charging power, recorded using a data logger. Tests were conducted for 30 minutes under varying solar radiation intensities. The results indicate that the acrylic cover reduces the panel’s output power, from 55 W to 45 W at a solar radiation intensity of approximately 1100 W/m². These findings suggest that the use of an acrylic cover must be carefully considered to maintain optimal charging system performance.

With financial inclusion acting as a moderator, the goal of this study is to examine how financial technology use and financial literacy impact microbusiness performance. Microbusinesses in Bandar Lampung City's food and beverage industry serve as the research subjects. This study employs a quantitative approach, using questionnaires to collect primary data from microenterprises. Purposeful sampling was used to choose 203 microbusinesses that met the research criteria. SPSS was utilized to analyze the data using multiple linear regression and Moderated Regression Analysis (MRA). The study's conclusions imply that the use of financial technology has an effect on microenterprises' performance. Additionally, it has been shown that financial literacy affects microenterprise performance. The findings show that key components in increasing microbusiness performance are the use of financial technology and the entrepreneur's capacity to supervise and make financial decisions. However, the test results indicate that financial inclusion cannot boost the impact of financial technology and financial expertise on microbusiness performance. This implies that the influence of financial technology and financial knowledge on business success is not necessarily enhanced by having access to financial services. It also shows that a key factor in increasing the success of microbusinesses is the characteristics of the entrepreneur. The research's objectives are to assist important stakeholders in creating plans for microenterprise growth, as well as to assist microenterprise actors in improving their financial literacy and utilizing financial technology to its fullest.

Brebes Regency is one of the main national centers of shallot production in Indonesia, where agricultural land dynamics play a crucial role in sustaining production performance. Rapid infrastructure development and land-use change have raised concerns regarding the sustainability of shallot cultivation areas. This study aims to analyze spatial–temporal changes in shallot cultivated area and production in Brebes Regency during 2023–2024 and to examine the relationship between land area changes and production levels at the sub-district scale. A descriptive quantitative approach based on spatial analysis was employed using Geographic Information Systems (GIS). Spatial data consisted of administrative boundary maps, while non-spatial data included shallot cultivated area and production data obtained from the Brebes Regency Office of Agriculture and Food Security. Data integration and analysis were conducted using QGIS through attribute joining and inter-annual comparison. The results indicate that total shallot cultivated area increased from 26,331 ha in 2023 to 28,628 ha in 2024, accompanied by a significant rise in production from 289,942.05 tons to 409,106.90 tons. Spatial analysis reveals that major production centers remain concentrated in the central and northern sub-districts, particularly Wanasari, Larangan, Bulakamba, and Tanjung. Although a positive relationship between land expansion and production increase is evident, variations among sub-districts suggest that productivity and local conditions also play important roles. The findings highlight the effectiveness of GIS-based analysis in supporting spatially informed agricultural land management and policy formulation.

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.

This study compares Grammarly and Paraphraser.io in supporting students’ academic writing and rewriting skills based on Flower and Hayes’s (1981) cognitive process theory, which includes the stages of planning, translating, and reviewing. Using a descriptive qualitative method supported by quantitative data, the research involved 32 English Education students who had used both tools in academic writing. Data were collected through questionnaires and analyzed descriptively to identify students’ perceptions of grammatical accuracy, rewriting ability, feedback quality, and overall writing improvement. The results of this study indicate that 54.4% of students use Grammarly and 45.6% use Paraphraser.io, showing that Grammarly is the slightly more dominant and trusted tool for improving grammatical accuracy and clarity, while Paraphraser.io functions as a complementary platform for paraphrasing and generating new sentence structures; together, both tools contribute effectively though through distinct roles to enhancing students’ academic writing performance. The comparative analysis revealed that Grammarly was most effective in enhancing analytical skills during the planning and reviewing stages, improving grammar, coherence, and clarity. In contrast, Paraphraser.io was most beneficial for generative skills in the translating stage, directly enhancing originality and sentence variation. Most respondents rated their writing improvement highly, confirming the positive impact of both tools on academic performance. In conclusion, the findings confirm that Grammarly and Paraphraser.io serve distinct, yet complementary roles: the former ensures linguistic precision, while the latter supports structural creativity, ultimately helping students produce highly accurate and original academic texts.

This study aims to conduct a Systematic Literature Review (SLR) regarding the influence of career development on organizational commitment. Based on the analysis of various scientific articles, career development is an important approach in human resource management, which focuses on improving employees’ skills, enthusiasm, and readiness to face job challenges and promotion opportunities. On the other hand, organizational commitment refers to the emotional connection that employees have with their workplace, which includes aspects of emotional attachment, obligation, and enduring bonds. The results of the systematic literature review indicate that many previous studies have found a strong and positive correlation between career development and organizational commitment. Workers who are aware of support for their career development, including training programs, mentoring, or predetermined career paths, generally show a higher level of dedication to their organization. Furthermore, various studies have shown that career development can also have an indirect effect on organizational commitment through mediating factors such as job satisfaction, work motivation, and employee performance. However, several studies have shown that career development does not always have a significant impact on organizational commitment, particularly in organizations without a structured career system or when employees have negative perceptions of career opportunities. These varying results highlight the existence of contextual factors that influence the relationship between variables. Overall, this study offers in-depth insights into the significance of career development as a strategic factor in enhancing organizational commitment, along with suggestions for organizations and future researchers conducting similar studies.

This study aims to analyze the effect of transformational leadership (X1), work experience (X2), and social support (X3) on employee performance (Y) at PT Putra Sentosa Mandiri. This research also seeks to determine the partial effects of each independent variable as well as their simultaneous effect on employee performance. The research method used is a quantitative approach with primary and secondary data sources. The sampling technique applied was saturated sampling, in which the entire population was used as the sample, consisting of 34 employees of PT Putra Sentosa Mandiri. Data were collected through questionnaires, interviews, literature studies, and documentation. The data analysis techniques included validity tests, reliability tests, normality tests, linearity tests, heteroscedasticity tests, multicollinearity tests, multiple linear regression analysis, t-tests, F-tests, and the coefficient of determination (R²). The results indicate that transformational leadership has a positive but not significant effect on employee performance, work experience has no positive and insignificant effect on employee performance, while social support has a positive and significant effect on employee performance. Simultaneously, transformational leadership, work experience, and social support do not have a significant effect on employee performance at PT Putra Sentosa Mandiri.

This study examines the impact of financial performance indicators—activity, solvency, and liquidity ratios—on profit growth in pulp and paper manufacturing companies listed on the Indonesian Stock Exchange from 2019 to 2024. The research focuses on how variations in Total Assets Turnover, Inventory Turnover, Fixed Assets Turnover, Debt to Equity Ratio, and Quick Ratio affect profitability, especially during periods of economic instability like the COVID-19 pandemic. The aim is to identify which financial ratios have the most significant influence on profit performance. A quantitative research method was employed, utilizing secondary data from 42 observations of seven manufacturing firms, selected through purposive sampling. Multiple linear regression analysis, supported by SPSS software, was used to test the hypotheses. The findings show that all five ratios collectively have a significant impact on profit variations, with an F-statistic of 2.568 and a significance value of 0.044. However, when tested individually, only Total Assets Turnover and Inventory Turnover showed significant effects, while Fixed Assets Turnover, Debt to Equity Ratio, and Quick Ratio did not. The coefficient of determination (R²) was 0.263, indicating that 26.3% of the variation in profit can be explained by the analyzed variables.