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Local government budget management is a crucial element in regional governance, as it directly impacts accountability, transparency, and efficiency in public service delivery. To support effective regional financial management, the Indonesian Government has established the Government Internal Control System (SPIP), as stipulated in Government Regulation Number 60 of 2008. This study aims to examine the role and practical implementation of SPIP in regional budget management through a case study of the Regional Financial and Asset Management Agency (BPKAD) of Dumai City. Using a qualitative case study approach, this study analyzes regional financial documents, audit reports from the Regional Audit Agency (BPKAD), as well as laws and regulations and internal policies governing SPIP implementation. The results indicate that SPIP has been implemented in BPKAD Dumai City throughout the budget management cycle, from planning and implementation to reporting and accountability. However, its implementation has not reached an optimal level due to constraints such as limited leadership commitment, inadequate human resource capacity, and suboptimal internal oversight mechanisms. Therefore, improving SPIP implementation is a strategic step to realize accountable, transparent, and performance-oriented regional financial governance.

This study explores the impact of work–life balance on job performance among Generation Z employees in Jakarta, with job satisfaction as a mediating variable and family-supportive supervisor behavior as a moderating variable. Generation Z, the largest segment of Indonesia's workforce, values flexibility and supportive leadership, but their job performance often shows inconsistency. Previous studies have yielded mixed results on the relationships between work–life balance, job satisfaction, and job performance, with limited empirical evidence on the role of family-supportive supervisor behavior, particularly in Indonesia. Using a quantitative approach, data were collected from 385 Generation Z employees in Jakarta through purposive sampling. Structural Equation Modeling (SEM) with SmartPLS 4.0 was used for data analysis. Results indicate that work–life balance positively influences both job satisfaction and job performance. Additionally, job satisfaction partially mediates the relationship between work–life balance and job performance, suggesting that a better work–life balance enhances job satisfaction, which in turn improves performance. The study also shows that family-supportive supervisor behavior significantly strengthens the positive effect of work–life balance on job performance. These findings contribute to the literature by clarifying how work–life balance affects job performance among Generation Z employees and highlighting the importance of supportive leadership.

This study aims to examine the effect of job rotation, physical work environment, and spiritual motivation on the performance of amil employees at BAZNAS Tanjungpinang City. This research adopts a quantitative approach using a survey method. The population consists of all amil employees at BAZNAS Tanjungpinang City totaling 35 respondents, and a census sampling technique is applied. Data were collected through questionnaires and analyzed using multiple linear regression with SPSS version 27. The partial test results indicate that job rotation and the physical work environment do not have a significant effect on amil employees’ performance. In contrast, spiritual motivation has a positive and significant effect on performance. Simultaneously, job rotation, physical work environment, and spiritual motivation collectively have a significant effect on the performance of amil employees at BAZNAS Tanjungpinang City. These findings highlight the important role of spiritual motivation in improving employee performance, particularly within zakat institutions that are strongly grounded in Islamic values.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.

Motorcycles are one of the main modes of transportation widely used by the Indonesian people due to their practicality, efficiency, and ability to support daily mobility. However, many motorcycle users complain about the suboptimal engine power, especially when the vehicle is used on extreme terrains outside urban areas, such as uphill roads, rocky paths, or long distances. This situation has driven the development of various innovations in the automotive field to improve engine performance. One widely applied method is the oversize technique on the piston and piston block, which aims to increase the combustion chamber capacity, thereby maximizing the power output. This study aims to analyze the significant comparison of engine performance after the application of the oversize method on the KVY engine type, as well as to examine the factors influencing the optimization of engine performance based on the philosophy and needs of the vehicle owner. The testing method used a dynamometer to measure the increase in torque and engine power. The test results showed that the daily oversize application resulted in a torque increase of 1.19 Nm and a power increase of 0.85 Hp compared to the standard condition. Meanwhile, the oversize for competition needs showed more significant improvements, with a torque increase of 7.97 Nm and a power increase of 8.12 Hp. These findings demonstrate that the oversize method can significantly enhance engine performance according to its intended use.

This study investigates the relationship between design patterns, modular architecture, and the maintainability of distributed real time systems developed using agile practices. Distributed real time systems are critical in various sectors, including telecommunications, healthcare, and automotive, where strict timing constraints and reliability are essential. Agile methodologies, known for their flexibility and iterative development, have been widely applied to software engineering, but their impact on long-term system maintainability, especially in complex real time environments, has been insufficiently explored. This research employs an empirical analysis, combining both quantitative and qualitative data from multiple real time system projects using agile methods. The analysis focuses on the application of design patterns, such as Singleton, Observer, and Factory, and evaluates the effectiveness of modular architectures in enhancing system scalability, flexibility, and long-term sustainability. The study also explores how agile practices contribute to system performance and maintainability, despite challenges related to frequent updates and coordination among distributed teams. Key findings show a positive correlation between the consistent use of design patterns and modularity, which significantly improves the maintainability and adaptability of distributed real time systems. This research also highlights the challenges faced by agile methods in maintaining architectural consistency and managing non-functional requirements, particularly in distributed environments. The results contribute valuable insights into adapting agile practices to meet the specific demands of distributed real time systems, offering recommendations for developers and project managers to incorporate modular architecture and design patterns to enhance long-term system sustainability. Further research is suggested to explore new design patterns and investigate the broader impact of agile methodologies on system quality beyond maintainability.

Anxiety in completing a final thesis is a common issue experienced by final-year students, which can affect academic performance, mental health, and even delay graduation. This condition highlights the importance of effective strategies to manage psychological pressure appropriately. This study aims to explore the application of spiritual coping in helping students reduce anxiety during the thesis writing process. The research employed a qualitative approach with a descriptive design. The participants consisted of three students selected from a total of thirteen who had been identified with severe anxiety. The instruments used were the Taylor Manifest Anxiety Scale as an initial assessment, followed by in-depth interviews and observations to explore their experiences, coping strategies, and psychological dynamics. Data were analyzed thematically by focusing on the personal, social, environmental, and religious domains in the practice of spiritual coping. The findings indicate that spiritual coping assists students in managing emotions, discovering life meaning, and maintaining academic commitment. Religious practices, prayer, spiritual reflection, and social support proved effective in fostering inner peace and alleviating anxiety symptoms. In conclusion, spiritual coping can serve as an effective strategy to support students in facing academic pressures and in enhancing psychological well-being.

The development of social media has shifted the way humans interpret spirituality and construct self-identity, giving rise to the phenomenon of digital religiosity that is all-visual, instant, and performative. This study aims to analyze how representations of spirituality in social media culture contribute to the crisis of self in modern humans, by interpreting this phenomenon through the metaphysical perspective of Seyyed Hossein Nasr. Using a qualitative approach with a descriptive-analytical design that enriches digital ethnography, this study collects data through documentation of spirituality-themed content on TikTok, Instagram, and YouTube, as well as a literature review of Nasr's works and literature related to digital spirituality. Thematic analysis shows that spirituality in social media is formed through symbolic aestheticization, the commodification of religious values, and identity performances oriented towards algorithms and public validation. These findings demonstrate the symptoms of the desacralization of modernity as criticized by Nasr, namely the erosion of spiritual depth due to the dominance of images and the narrowing of transcendent meaning. This study emphasizes that social media is not just a medium, but a space for the formation of consciousness that can facilitate and endeavor the spiritual search of modern humans. Theoretically, this research contributes to the study of digital spirituality and the critique of modernity; In practice, he encourages more critical digital literacy so that people can manage spiritual experiences more authentically.

This community service activity aims to strengthen the marketing strategy of FUNN Maumere as a culinary business operating in an increasingly competitive market environment. The program was conducted using a qualitative descriptive approach through field observations, interviews with business owners and employees, and direct mentoring in implementing more effective marketing strategies. The results indicate that FUNN Maumere has applied a combination of traditional and digital marketing strategies, including brochure distribution and active promotion through social media platforms such as Instagram, TikTok, and Facebook. The mentoring activities focused on optimizing digital marketing, improving service quality, and strengthening brand consistency. These efforts contributed positively to increased brand awareness, customer interest, and wider promotional reach. However, sales performance remains fluctuative due to external factors, such as weather conditions and differences in outlet location characteristics. Therefore, this community service emphasizes the importance of continuous product innovation, enhancement of customer experience, and optimization of digital marketing strategies to reduce dependency on external conditions. This activity is expected to serve as a practical reference and mentoring model for other culinary MSMEs in developing adaptive, innovative, and sustainable marketing strategies to improve competitiveness and business continuity.

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 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.

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.

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.

Big data platforms face significant challenges related to cybersecurity and privacy due to the vast volume, variety, and velocity of data they manage. Traditional static security measures often fail to address the dynamic and complex nature of big data environments. This research proposes an adaptive cybersecurity framework that integrates dynamic access control and differential privacy mechanisms to enhance both the security and privacy of big data platforms. The dynamic access control mechanism continuously adjusts access permissions in real-time based on changing risk and trust levels, ensuring that sensitive data remains secure even as user roles and data flows evolve. The differential privacy mechanism adds noise to data, preserving individual privacy while allowing for meaningful data analysis. Through simulations and case studies, the framework was evaluated in various real-world environments, including healthcare, IoT, and finance, where it demonstrated scalability, efficiency, and robust security performance. The results showed that the proposed framework significantly reduced unauthorized access attempts and maintained data privacy, while still enabling effective data analysis. Although there were some challenges regarding performance overhead, particularly in resource-constrained environments, the framework remained effective in large-scale systems. The findings highlight the importance of adaptive security practices in big data environments and suggest that future research should focus on refining dynamic security mechanisms and applying differential privacy in diverse real-world scenarios. These advancements are essential for ensuring that big data platforms can handle evolving cyber threats without compromising data utility or privacy.

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.

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

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.