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This study aims to analyze the inventory management practices of qurban cattle at Purnomo Sapi Mulyo Farm in Boyolali, Central Java, particularly in facing the surge in demand prior to Eid al-Adha. The research is motivated by the unique characteristics of qurban cattle inventory, which involves living assets, seasonal demand, and biological risks that differ significantly from conventional inventory management. A qualitative descriptive approach with a case study design was employed to capture in-depth information regarding inventory planning, procurement, storage, and sales practices. Data were collected through in-depth interviews with the business owner as the key informant, direct observation of operational activities, and documentation review. The findings reveal that inventory management at the farm is conducted in a responsive manner based on consumer orders, enabling the business to minimize overstock risks and operational costs. However, inventory recording remains manual and unstructured, potentially limiting the accuracy of cost calculation and long-term planning. Price fluctuations and supply availability are strongly influenced by the Eid al-Adha momentum, while cattle health and lead time are critical factors affecting inventory effectiveness. From a theoretical perspective, the study extends inventory management concepts to the context of live and seasonal inventory. Practically, the findings suggest that implementing a simple yet structured inventory recording system could enhance operational efficiency and decision-making accuracy in local qurban cattle farms.

This study examines the role of the General Affairs (GA) Division in ensuring the effectiveness of administrative processes and facility management at PT. Panorama Indah Permai (Saloka Theme Park). The research was conducted during an internship program from September to December 2025, during which the researcher was directly involved in daily operational activities. Using a descriptive qualitative method, data were obtained through direct observation, administrative document analysis, and informal interviews with GA personnel. The results show that the GA Division functions as a critical support unit responsible for logistics management, documentation control, meeting room preparation, and operational vehicle administration. Several challenges were identified, including inconsistencies in documentation, high workload during peak operations, and limited digital integration. Despite these challenges, the GA Division plays a central role in coordinating with other departments to maintain workflow continuity. This study reinforces the relevance of operations management, office administration, facility management, and organizational communication theories in practical workplace settings. The findings imply that improving digital administrative systems, enhancing coordination mechanisms, and standardizing procedures could significantly strengthen GA performance and overall organizational efficiency.

This study aims to analyze the effectiveness of the internal control system in the Personnel Division of PT Sendang Derma Pesona in supporting effective, efficient, and well-governed human resource management. The background of this research is based on the importance of internal control systems in preventing administrative errors, reducing the risk of fraud, and ensuring compliance with labor regulations, particularly in personnel and payroll functions. This study employs a descriptive qualitative approach using an operational audit method. Data were collected through questionnaires, interviews, and document observation covering recruitment procedures, personnel data management, employee transfers, authorization processes, as well as payroll and bank reconciliation procedures. The results indicate that, in general, the internal control system in the Personnel Division has been implemented adequately, as reflected by complete personnel documentation and multi-level authorization in decision-making processes. However, several weaknesses were identified, including the lack of proper segregation of duties between payroll preparation and salary payment, the absence of regular bank reconciliation, and the continued use of manual personnel archive management. These weaknesses indicate that the internal control system still needs to be strengthened to minimize the risk of errors and improve the efficiency of personnel administration. The findings of this study are expected to provide practical implications for management in improving internal control policies and procedures to support better corporate governance and sustainable organizational performance.

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.

Increasing operational efficiency in power generation systems is crucial in overcoming the increasing energy demand, especially in the industrial sector. One solution to improve the efficiency is to optimize the use of economizer in the boiler of Steam Power Plant (PLTU). This study analyzes the impact of using an economizer in a 240 ton/hour capacity CFB boiler at PT X on system efficiency and fuel consumption. The results of the analysis show that the use of an economizer can increase the average feed water temperature from 207.73°C to 298.96°C, with a temprature increase of 91.23°C. This temperature increase reflects the economizer's ability to utilize flue gas heat to heat the feed water, which has an impact on reducing fuel consumption by 17.18%, from 45716.56 kg/hour to 37862.50 kg/hour. The boiler efficiency also increased significantly, from 68.36% to 82.54%, which shows the positive impact of the economizer on boiler performance. This study concludes that the use of economizer can improve energy efficiency and reduce fuel consumption, and is recommended to be applied to large capacity boiler systems to optimize energy savings and operational efficiency. Further research is needed to explore the influence of other variables on economizer and boiler performance in more depth.

This study aims to review the perspectives of MSME owners in the Gonilan area, particularly regarding part-time workers and their use in improving operational cost efficiency. The Gonilan area is known to have many MSMEs operating in various sectors, making this study relevant for understanding the challenges they face. A qualitative method was chosen so that the researcher could conduct in-depth interviews with three MSME owners. The informants were selected because they employed both full-time and part-time workers. The interview results showed that part-time workers were effective in reducing operational costs, such as more flexible hourly wages, although there were some disadvantages. The main disadvantages of part-time workers included lack of experience and limited working hours, which could be overcome through extra training and clear task distribution so that they could focus and increase productivity. The study concluded that the use of part-time workers has a significant impact on the operational cost efficiency of MSMEs, helping owners navigate the market without excessive financial burdens and encouraging long-term business growth.

This study examined the operational performance of CV Fortis Sportwear Indonesia (FSI) before implementing the Balanced Scorecard approach, identified the factors that hindered each Balanced Scorecard perspective, and analyzed the company's operational performance when measured using this approach.This study employed a descriptive quantitative method, aimed at providing a systematic, factual, and accurate overview of CV FSI's operational performance based on the Balanced Scorecard approach. Data were collected through interviews, questionnaires, observations, and literature review to obtain information related to the four main perspectives of the Balanced Scorecard: financial, customer, internal business processes, and learning and growth.After implementing the Balanced Scorecard, the performance evaluation of CV Fortis Sportswear Indonesia became more comprehensive compared to the previous system, which focused solely on financial aspects. The financial perspective showed improvement, but the customer aspect remained adequate, with declining retention and increasing complaints. Internal business processes still faced inefficiencies and quality issues, despite the introduction of new technology. The learning and growth perspective also improved through training, but employee satisfaction remained moderate. The Balanced Scorecard proved to provide a more comprehensive performance assessment and identified the need for improvements in customer, internal processes, and human resource development.

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.

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.

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.

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.

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.

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.

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.