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This study is motivated by the importance of financial and asset management strategies in supporting the operational effectiveness of the Regional Disaster Management Agency (BPBD) of Central Sulawesi, which plays a strategic role in disaster mitigation, preparedness, emergency response, and post-disaster recovery. The main problems addressed are how financial and asset management strategies are implemented, to what extent they affect operational effectiveness. The objectives of this research are to analyze the applied strategies, assess their influence on operational effectiveness, and identify challenges as well as relevant solutions.The literature review refers to public financial management theories, regional asset management, and previous studies highlighting the relationship between financial governance, accountability, and public sector performance. This study employs a quantitative approach with a descriptive design. Data were collected through literature study, observation, interviews, and questionnaires distributed to BPBD staff in finance and asset divisions. The analysis included validity and reliability tests, along with multiple linear regression to examine the effect of independent variables on operational effectiveness. The findings show that BPBD Central Sulawesi’s financial management strategy in 2024 achieved a realization rate of 89–100% in most programs, although imbalances were found in certain activities such as the disaster management system arrangement, which only reached 38%. In terms of asset management, fixed assets dominate with a book value of IDR 19.6 billion, with significant growth in equipment and machinery. Regression analysis results indicate an R² value of 0.817, meaning that 81.7% of operational effectiveness is influenced by financial and asset management strategies, while the remaining 18.3% is explained by other factors.The study concludes that financial and asset management strategies significantly affect BPBD’s operational effectiveness. Nevertheless, challenges such as limited human resources, inadequate information systems, and discrepancies in budget realization require solutions through capacity building, technology utilization, and improved planning mechanisms to optimize disaster management effectiveness.

This study aims to analyze the differences in motorcycle traffic flow (Q) during the morning and afternoon peak hours as an indicator of roadway operational performance, referring to the Indonesian Road Capacity Guidelines (PKJI) 2014, with a case study on Mawar Street–Wijaya Kusuma Street, Jember Regency. The research data were obtained from 12 observation points through traffic surveys that recorded motorcycle traffic flow in vehicles per hour (veh/h). The analytical methods used include descriptive statistical analysis, normality testing, and paired sample t-test. The results show that the average motorcycle traffic flow during the morning peak hour is 115.58 veh/h with a standard deviation of 62.97, while during the afternoon peak hour it is 63.25 veh/h with a standard deviation of 28.57. The paired sample t-test yields a significance value of 0.015 (p < 0.05), indicating a statistically significant difference between morning and afternoon traffic flows. These findings suggest that the level of roadway capacity utilization is higher during the morning peak hour, which is closely associated with dominant routine travel activities such as commuting to work and school. The results of this study are expected to serve as a basis for evaluating roadway operational performance and to support traffic management and traffic engineering planning aimed at improving road network performance and reducing congestion.

Administration is an activity that must be managed effectively to support public service quality, and one of the main keys to achieving a good administrative system is the utilization of information technology, which is currently developing rapidly. Administrative activities at the Wendewa Barat village hall are still carried out using conventional and ineffective methods, particularly in population data recording, which relies on manual bookkeeping. This approach is time-consuming, labor-intensive, prone to errors, and incurs higher operational costs. Therefore, an integrated system is needed to manage administrative activities more efficiently, accurately, and systematically. This research aims to develop a web-based population administration system to improve administrative performance at the Wendewa Barat village office. The system development employs the waterfall method, which consists of several stages, including requirements analysis, system design, implementation, system testing, and maintenance. The system is built using the PHP programming language with the CodeIgniter framework and utilizes a MySQL database for data management. Additionally, jQuery is applied in several system functions to enhance system interactivity, effectiveness, and user interface attractiveness.

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.

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.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

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.

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

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

In the context of both public organizations and Small and Medium Enterprises (SMEs), inefficient business processes remain a significant challenge. Fragmented information systems often hinder the optimization of these processes, leading to slower decision-making, redundant efforts, and increased operational costs. This study aims to analyze and optimize business processes by utilizing integrated information systems (IIS), providing a comparative analysis between the two sectors. The theoretical framework explores key theories such as Business Process Management (BPM) and the integration of information systems for process optimization. Previous studies highlight the differences in how IIS implementation impacts the public and SME sectors, noting challenges such as data silos, legacy systems, and resistance to change. A case study analysis methodology was employed to assess the effectiveness of IIS across both sectors. Business Process Modeling (BPMN) was used to visualize business processes before and after optimization, and process performance was measured through key metrics such as time reduction, error rates, and cost efficiency. The results show that IIS integration improved business process efficiency by an average of 28%, with reductions in redundancy and faster decision cycles observed in both sectors. Public organizations benefited from enhanced service delivery and better resource management, while SMEs gained competitive advantages through streamlined operations and increased responsiveness to market demands. The comparison reveals that integrated systems had a greater operational impact than traditional isolated process reengineering methods. Public organizations faced more regulatory and governance challenges, while SMEs leveraged their flexibility for faster integration. Recommendations for both sectors include focusing on overcoming barriers such as resistance to change and investing in system modernization. Future research should explore the long-term effects of IIS integration and further sector-specific comparisons.

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.

The City of Singkawang faces significant challenges in drinking water management, characterized by limited production capacity, high non-revenue water (NRW), and the absence of digital infrastructure such as AMR, LoRaWAN, and DMA, necessitating a structured development framework to support the transition toward a smart water system. This study formulates an IoT-based smart water roadmap aligned with the regional development plan (RPJMD) and the operational capacity of the local water utility through a performance gap analysis with benchmark cities (Surabaya and Balikpapan), capacity assessment using six objective parameters, and the use of secondary data from official reports and technical documents. The resulting roadmap comprises three sequential phases covering basic infrastructure reinforcement, network digitalization through IoT sensors and telemetry, and the implementation of DMA and SCADA to enable real-time monitoring and control. This approach provides a realistic and adaptive implementation framework for medium-sized cities with limited resources, strengthening NRW reduction efforts, improving service reliability, and supporting the integration of digital technologies in sustainable water utility management.

Steam turbines remain a core technology in thermal power generation and continue to evolve through advances in aerothermal design, materials, control strategies, and digital maintenance. This paper presents a systematic literature review (SLR) of recent international studies published between 2020 and 2025 to synthesize current developments in steam turbine performance and thermal efficiency improvement. Article identification was conducted through SCOPUS using keywords related to turbine efficiency, blade/nozzle optimization, failure analysis, and material enhancement. The selected studies were analyzed thematically across four domains: (1) design and optimization using CFD/FEA, (2) material and structural resilience, (3) operational performance under variable/part-load conditions, and (4) integration with hybrid renewable systems and predictive maintenance. The reviewed evidence indicates that CFD-based nozzle/blade optimization and advanced control approaches can yield measurable efficiency improvements (approximately 2–7.3%), while material innovations and enhanced cooling strategies improve durability by mitigating thermal stress and fatigue risks. In parallel, digitalization through IoT-based predictive maintenance and additive manufacturing is increasingly reported as a pathway to reduce downtime and accelerate component production. However, recurring gaps include limited real-world validation, insufficient studies in humid/tropical environments, and a lack of long-term economic/lifecycle assessments. Future work should prioritize experimental or field verification, region-specific performance studies, and integrated techno-economic evaluation to support broader deployment of high-efficiency steam turbine systems.

The rapid development of the sea transportation industry produces a massive and complex volume of transaction data, requiring strategic management to support managerial decision-making. This research aims to implement the Executive Information System on SeaPass in order to evaluate the performance of ship ticket sales. The research method uses data visualization with a two-level drill-down mechanism, which allows the presentation of information hierarchically from general summaries to specific details. The methodological stages include needs analysis, user interface (UI) design using Figma, front-end implementation with HTML, CSS, and JavaScript, database integration, and system testing through Black Box Testing. The results showed that the SIE implementation successfully integrated operational data, including schedules, ships, and manifests, into an interactive dashboard. The two-level drill-down feature provides the ability for executives to identify operational anomalies and market fluctuations in real-time. In conclusion, the system significantly enhances executive data analysis capabilities, transforming complex transaction data into accurate strategic information, thereby supporting more precise business decision-making and adaptive to the dynamics of the marine transportation market.

The city of Singkawang has experienced rapid population growth, leading to an increase in the number of students. On 17 September 2024, the Singkawang City Transportation Agency implemented a free revitalized Student Transport service to reduce the number of traffic accidents involving students. The aim of this study is to identify service and respondent characteristics, evaluate operational management, and assess performance and user satisfaction using the IPA and CSI methods, as well as Vehicle Operating Costs (VOC). The research employs a descriptive quantitative method by analyzing descriptive statistical data. Data were obtained from field observations, interviews, and surveys of 400 student respondents (146 users and 254 non-users). The results show that the student transport operates with two vehicles serving the North and East Singkawang routes. The load factor for outbound trips is 22%–32% and for return trips is 11%–12%, with travel times of 68–85 minutes, average operating speeds of 20–22 km/hour, and circulation times of 68–85 minutes. Based on the IPA analysis, the indicators in quadrant D require socialization regarding the functions and use of the interior of the student transport. The CSI result shows a score of 99.79% (very satisfied). The annual VOC amounts to IDR 292,905,814 (East Singkawang) and IDR 282,020,390 (North Singkawang). In conclusion, this service is satisfactory but still requires socialization to enhance its attractiveness and effectiveness.

Traditional snack micro, small, and medium enterprises (MSMEs) in Indonesia face increasing market competition and rapidly changing consumer preferences, particularly among younger consumers seeking innovative and symbolic food experiences. This community engagement study aims to strengthen the profitability and sustainability of traditional snack MSMEs by integrating local flavor innovation with simple business governance practices. Using a participatory action research approach under the Merdeka Belajar Kampus Merdeka (MBKM) program in Palangka Raya, this study involved co-creation between students and local entrepreneurs in product development, production standardization, and basic financial management. Qualitative data were collected through participatory observation and stakeholder discussions, while quantitative data were obtained from sales records and simple financial reports. The results demonstrate that local flavor-based innovation, combined with standardized operating procedures and cost control mechanisms, improved product differentiation, operational efficiency, and financial performance. The intervention generated a positive net profit and strengthened the partner’s capacity for independent business management. This study contributes to the literature by positioning traditional food MSMEs as sites of cultural innovation and micro-governance, while supporting Sustainable Development Goals related to inclusive economic growth, cultural preservation, and responsible production.

The financial crisis experienced by PT Garuda Indonesia Tbk prompted the company to enter into a Debt Payment Suspension (PKPU) process as a legal measure to avoid bankruptcy and restructure its finances. The complexity of debt, liquidity pressures, and post-pandemic operational challenges have made PKPU a strategic instrument for obtaining debt payment deferrals and formulating a settlement plan that is acceptable to creditors. This study aims to analyze how the implementation of PKPU affects Garuda's rescue efforts from the threat of bankruptcy and assess the extent to which the debt restructuring resulting from PKPU in 2021–2023 effectively improves the company's financial condition. The method used is normative legal research with a legislative, conceptual, and case study approach to the homologation decision and Garuda Indonesia's official financial reports. The results of the study show that PKPU provides legal certainty for debtors and creditors through a collective postponement mechanism, and debt restructuring has been proven to significantly reduce the company's liabilities from around US$10.1 billion to around US$4.6 billion, while improving financial and operational stability in the short to medium term. In conclusion, PKPU serves as an effective corporate rescue instrument, while post-PKPU debt restructuring provides a strong foundation for Garuda Indonesia's financial recovery, although long-term sustainability still depends on the consistent implementation of the peace plan and the company's operational performance.

This study examines the relationship between risk disclosure levels and profitability in transportation companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. The transportation industry is characterized by high exposure to operational, financial, regulatory, and market-related risks, which necessitates transparent and comprehensive communication regarding potential threats to business sustainability and long-term performance. Using a quantitative correlational approach, this study measures the level of risk disclosure through systematic content analysis of companies’ annual reports, while profitability is evaluated using the Return on Assets (ROA) indicator. The analysis is conducted to identify the extent to which transparent risk reporting contributes to improved financial outcomes. The findings indicate a significant positive relationship between risk disclosure and profitability, suggesting that companies providing more comprehensive and detailed risk information tend to achieve higher financial performance. Furthermore, the results demonstrate that transparency plays a crucial role in enhancing investor confidence, strengthening corporate governance, and supporting sustainable business practices, particularly in high-risk sectors such as transportation.