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This study aims to explore the institutional governance and collaboration mechanisms at Indonesia's entry points, emphasising both formal arrangements, such as Memoranda of Understanding, and informal communication channels that facilitate daily operations among Immigration, Customs, Port Authorities, Law Enforcement, and Health Agencies. The research employed qualitative methods, including interviews, observations, and document analysis, grounded in Policy Network theory, to analyse the dynamics of institutional collaboration at entry points. The findings highlight that border oversight effectiveness hinges on inter-agency synergy quality, trust, and the ability to overcome technical and procedural barriers, whilst significant challenges include technological disparity, particularly incompatible data platforms hampering real-time information sharing, and silo mentalities driven by security concerns that impede operational harmonisation, resulting in delays and inefficiencies. Trust mechanisms such as informal communication groups, regular meetings, and leadership support significantly improve coordination and operational performance. The study concludes that sustainable and adaptive governance models characterised by mutual trust, effective communication, and technological integration are essential for enhancing border security and facilitating legal movement. The research underscores the importance of integrating technological systems to ensure interoperability, developing clear cross-agency SOPs, and fostering a collaborative culture that prioritises shared goals over organisational ego. Strengthening institutional collaboration at border crossings will bolster Indonesia's national security, economic growth, and international reputation, and contribute to resilient, efficient border management systems capable of addressing contemporary threats.

The study aims to improve the passion fruit syrup production system at Harisma Home Industry, which has been hindered by an inefficient facility layout and complicated raw material flow. These issues lead to longer processing time, higher operator workload, and reduced production capacity. To solve this, the research uses the Activity Relationship Chart (ARC) and Total Closeness Rating (TCR) to evaluate and redesign the layout. TCR results show that the Raw Material Warehouse has the highest priority with a score of 119, requiring an Absolutely Important (A) relationship with the Production Room. The Parking Area and Waiting Room also fall into the Absolutely Important category, as they support smooth movements of materials and visitors. Meanwhile, the Toilet has a Very Important (E) relationship with the Waiting Room due to comfort and accessibility needs. Based on these proximity requirements, a new layout was designed by placing the Raw Material Warehouse next to the Production Room to create a faster and more efficient material flow. Other facilities were rearranged to minimize unnecessary movement. This new layout is expected to streamline processes, reduce congestion, and enhance overall capacity and operational performance.

The increasing integration and digitization of smart grid systems have exposed them to a variety of security threats, necessitating robust security measures to ensure their reliability and efficiency. This paper proposes a novel Digital Twin-Based Cyber-Physical Security Framework, incorporating AI-driven predictive maintenance and zero-trust architecture to address the evolving challenges of securing smart grids. By leveraging digital twin technology, this framework creates a real-time virtual representation of physical systems, enabling continuous monitoring and simulation for enhanced security and operational performance. Zero-trust security principles are integrated to ensure that no entity, whether inside or outside the network, is trusted by default, thus significantly reducing the risk of cyber-attacks. Additionally, AI-driven predictive maintenance enhances the framework’s reliability by proactively identifying potential failures before they occur, reducing downtime and improving system resilience. Through the development and simulation of this framework, including attack and failure scenarios, the paper demonstrates that the proposed system outperforms traditional methods in terms of anomaly detection, system downtime, and response times. The integration of predictive maintenance allows for early identification of component failures, thus enhancing the overall resilience of the grid. The zero-trust architecture further strengthens the cybersecurity posture, preventing unauthorized access and attacks. The study also identifies challenges, such as data synchronization and scalability, which must be addressed for broader implementation in large-scale smart grid systems. The findings suggest that the proposed framework could play a critical role in the future evolution of smart grid security, offering valuable insights for researchers and practitioners.  

This study aims to analyze the effect of transformer oil purification on oil breakdown voltage and evaluate its benefits in maintaining transformer performance and reliability. Oil purification is a crucial preventive maintenance step to preserve insulation quality and prevent operational failures caused by reduced dielectric properties. The study was conducted through several stages, including data collection, oil purification, measurement of breakdown voltage before and after purification, and evaluation of results. Data collection involved measuring the oil’s electrical properties according to SPLN 49-1:1982 and observing results using a Break Down Voltage (BDV) test. Purification of a Trafindo 400 kVA transformer was performed through visual inspection, connecting the inlet and outlet hoses to the purification machine, and circulating the oil until the breakdown voltage met the required standards. Results indicated that the oil breakdown voltage before purification was below standard due to reduced insulation quality caused by water contamination, charcoal particles, thermal degradation, and dissolved gases that weakened dielectric properties. Additional factors such as electrical stress, mechanical stress, and excessive loading also contributed to insulation deterioration. After purification, the oil breakdown voltage increased significantly to meet the standard of >30 kV/2.5 mm, demonstrating that purification effectively restores the oil’s insulating capacity and supports optimal transformer performance. Analysis confirms that the transformer oil remains suitable for use, and routine annual purification is recommended to maintain reliability, efficiency, and operational performance. This study highlights oil purification as an effective preventive measure for transformer stability, extending operational life, and reducing the risk of insulation failure. The findings provide valuable guidance for transformer maintenance in the electricity industry, ensuring safe and optimal long-term operation.

This study explores the key determinants of field engineer efficiency in the field service industry by analyzing the impact of self-efficacy, resource management, and time management on operational performance. Employing a quantitative research approach, data were collected using saturated sampling from 102 field engineers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The measurement model showed robust psychometric properties, satisfying the thresholds for convergent validity, discriminant validity, and internal consistency reliability. The structural model results indicate that all three independent variables significantly influence field engineer efficiency. Self-efficacy was found to have the strongest effect (β = 0.421, p < 0.001), followed by resource management (β = 0.347, p < 0.001) and time management (β = 0.289, p < 0.001). The model accounts for 68.7% of the variance in field engineer efficiency, reflecting strong explanatory power and predictive accuracy. Among these variables, self-efficacy emerged as the most dominant factor, suggesting that field engineers' belief in their ability to perform tasks is a critical driver of operational success. High self-efficacy enhances motivation, resilience, and effective problem-solving under pressure, making it essential in dynamic and unpredictable field environments. Resource and time management also play crucial roles in supporting engineers' ability to complete tasks efficiently by ensuring optimal allocation of tools, equipment, and time. The findings provide practical implications for field service organizations aiming to improve workforce performance. Investing in training programs that strengthen self-efficacy, combined with systematic improvements in resource and time management practices, can significantly enhance operational outcomes. By prioritizing these factors, organizations can boost engineer efficiency, reduce operational costs, and improve service delivery, ultimately gaining a stronger competitive advantage in the industry.

The rapid development of information technology and telecommunications has driven companies and MSMEs to adopt desktop, mobile, and web-based applications to enhance operational performance. PT HPS Painan Padang, previously relying on manual ticket bookings, faced challenges in efficiency and susceptibility to brokering practices. To address this, a web-based travel ticket booking application was developed, enabling customers to book tickets online more practically, quickly, and conveniently, anytime and anywhere. The integration of Natural Language Processing (NLP) technology in the form of a chatbot further enhances service efficiency and responsiveness. This feature provides real-time information on departure schedules, seat selection, and payment transactions while also supporting sentiment analysis and swift responses to customer feedback, ultimately improving service quality. The application, built using PHP, MySQL, and CodeIgniter with a Prototype development approach, adapts to changing market demands. Testing via GTmetrix on the https://desistravel.xyz/backend page demonstrated excellent performance with an 88% Performance score, 95% Structure score, and optimal Web Vitals metrics, indicating a fast, responsive, and stable platform. The final product is a user-friendly web-based ticket booking application supported by an intelligent chatbot, offering a seamless user experience, enhancing customer satisfaction, and streamlining the travel booking process efficiently.

The rapid development of information technology and telecommunications has driven companies and MSMEs to adopt desktop, mobile, and web-based applications to enhance operational performance. PT HPS Painan Padang, previously relying on manual ticket bookings, faced challenges in efficiency and susceptibility to brokering practices. To address this, a web-based travel ticket booking application was developed, enabling customers to book tickets online more practically, quickly, and conveniently, anytime and anywhere. The integration of Natural Language Processing (NLP) technology in the form of a chatbot further enhances service efficiency and responsiveness. This feature provides real-time information on departure schedules, seat selection, and payment transactions while also supporting sentiment analysis and swift responses to customer feedback, ultimately improving service quality. The application, built using PHP, MySQL, and CodeIgniter with a Prototype development approach, adapts to changing market demands. Testing via GTmetrix on the https://desistravel.xyz/backend page demonstrated excellent performance with an 88% Performance score, 95% Structure score, and optimal Web Vitals metrics, indicating a fast, responsive, and stable platform. The final product is a user-friendly web-based ticket booking application supported by an intelligent chatbot, offering a seamless user experience, enhancing customer satisfaction, and streamlining the travel booking process efficiently.

The structure of the Windows operating system plays an important role in supporting the operational performance of computers, especially in business environments and professional users. This system is designed with a modular and layered architecture, which includes various components such as Hardware Abstraction Layer (HAL), I/O device manager, virtual memory management, and an integrated security system. Windows also supports energy management and plug-and-play mechanisms, which improve power efficiency and hardware compatibility. This architectural approach allows Windows to optimize interactions between hardware and applications, supports multitasking, and provides an efficient user interface. Thus, the structure of the Windows operating system is able to significantly improve device performance, which leads to optimization of operational performance.