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Prolonged outpatient waiting time remains a major challenge in public hospitals and reflects inefficiencies in service process management. Internal medicine clinics are characterised by high service complexity and patient volume, making them particularly vulnerable to process waste that contributes to extended waiting times. Objective: This study aimed to develop an outpatient service flow model based on the Lean Hospital approach to reduce waiting time in the internal medicine clinic of RSUD Balaraja. Methods: A qualitative case study design was employed. Data were collected through direct observation of outpatient service processes, document review, and Focus Group Discussions (FGD) with relevant stakeholders. Process analysis was conducted using Value Stream Mapping (VSM) to identify value-added and non–value-added activities, while Fishbone Diagram analysis was applied to explore the root causes of process inefficiencies. All findings were integrated to develop a future-state outpatient service flow model. Results: The findings indicate that prolonged outpatient waiting time is primarily influenced by inefficient service flow design, repetitive administrative processes, and weak coordination among service units. The proposed service flow model demonstrates a more streamlined, integrated, and patient-centred process, with potential reductions in waiting time through systematic waste elimination and process improvement. Conclusion: The Lean Hospital approach provides an effective framework for developing outpatient service flow models in public hospitals. The proposed model offers a practical basis for improving outpatient service efficiency in complex clinical settings such as internal medicine clinics.

Congestive heart failure (CHF) is a cardiovascular disease that frequently causes shortness of breath (dyspnea) and negatively impacts patients’ quality of life. The management of dyspnea involves not only pharmacological approaches but also nonpharmacological interventions, one of which is handheld fan therapy, which works by stimulating sensory receptors in the facial area. This study aims to describe the effect of handheld fan therapy on reducing shortness of breath in patients with CHF. This study employed a descriptive design using a case report method on a single 63-year-old female patient diagnosed with CHF. The intervention was conducted over four days, involving the use of a handheld fan for 10 minutes, held at a distance of 15–30 cm from the face and directed toward the nose and mouth. Measurements were taken before and after the intervention using respiratory rate and oxygen saturation as parameters. The results showed a gradual decrease in respiratory rate from 24 breaths/minute to 18 breaths/minute, as well as an increase in oxygen saturation from 91% to 96% during the intervention period. These findings indicate an improvement in breathing patterns and increased respiratory efficiency in the patient. Handheld fan therapy is effective as a nonpharmacological intervention for reducing shortness of breath and improving oxygenation in patients with CHF. This intervention is safe, easy to use, and can be implemented as a complementary therapy in nursing practice.

. This study aims to analyze the role of field surveys in the verification process of mustahik as an effort to enhance accountability in zakat management at BAZNAS Kabupaten Tegal. Zakat management in modern institutions requires accurate targeting to ensure that distribution aligns with Islamic principles and effectively addresses social welfare issues. This research employs a descriptive qualitative approach, utilizing observation, in-depth interviews, documentation, and literature study as data collection techniques. The findings reveal that field surveys serve as a primary instrument in validating administrative data by directly observing the socio-economic conditions of prospective beneficiaries. This process enables more accurate and objective decision-making in determining mustahik eligibility. Furthermore, field surveys contribute significantly to improving transparency, accountability, and the precision of zakat distribution. However, the implementation of field surveys faces several challenges, including limited human resources, time constraints, and the potential for subjective assessment. Despite these limitations, field surveys remain a crucial operational mechanism in ensuring accountable zakat management. This study highlights the importance of strengthening survey implementation through capacity building and the integration of information technology to improve efficiency and data accuracy.

This study aims to evaluate the determinants of organizational sustainability within the Doctor Peduli Foundation (doctorSHARE), specifically regarding its Floating Hospital program. Specifically, it examines the influence of stakeholder support and operational efficiency on organizational sustainability, while analyzing the role of organizational innovation as a mediating variable in ensuring the continuity of medical services in remote areas. Employing a quantitative approach with a survey method, data were collected from 81 respondents comprising staff and volunteers directly involved in the Floating Hospital's operations. Structural Equation Modeling (SEM) was utilized to analyze the data and test the proposed hypotheses.The results reveal that stakeholder support does not have a significant direct impact on either organizational sustainability or innovation performance. Conversely, operational efficiency emerged as a dominant factor with a significant positive influence. Furthermore, organizational innovation was found to play a crucial role as a mediator, strengthening the impact of operational efficiency on organizational sustainability.This study concludes that the sustainability of non-profit organizations such as doctorSHARE is determined more by management's ability to optimize internal resources efficiently and innovatively than by reliance on external support. Consequently, strengthening internal operational systems should be a primary strategic priority to ensure long-term organizational existence.

The shaft is a crucial component in mechanical systems because it serves to transfer power and rotational motion throughout the machine. This research aims to assess the structural strength and operational performance of shafts used in a tire shredding machine through numerical simulation methods in order to achieve a safe and efficient design. The study involved several stages, including the development of shaft geometry models, the determination of boundary conditions, load application, mesh generation, and stress analysis using the finite element method. Two shaft configurations were examined: a 59 mm diameter shaft made from AISI 1045 steel and a 49 mm diameter shaft manufactured from ASTM A36 steel. The simulation results indicate that the 59 mm shaft experiences a Von Mises stress of 8.9 × 10⁻⁵ MPa, with a maximum displacement of 0 mm and a safety factor of 15. Similarly, the 49 mm shaft shows a Von Mises stress of 8.4 × 10⁻⁵ MPa, no measurable displacement, and a safety factor of 15. These findings confirm that both shaft designs are capable of safely withstanding the applied working loads. In addition, cutting system tests revealed that a 24-tooth blade achieved an efficiency of 26.9%, while a 40-tooth blade reached only 22.3%, indicating that the 24-tooth configuration provides better performance.

This study aims to analyze the effect of production costs and sales volume on profit with firm size as a moderating variable in food and beverage sector companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. This study used a quantitative approach with an associative research design. The data used were secondary data obtained from annual financial statements of food and beverage companies listed on the IDX. The sampling technique used purposive sampling and obtained 110 observations. The analysis method employed was Moderated Regression Analysis (MRA) using IBM SPSS Statistics. The results of the study indicate that production costs do not have a significant effect on company profit. Meanwhile, sales volume has a positive and significant effect on profit. Firm size has a negative and significant effect on profit. Furthermore, firm size is unable to moderate the relationship between production costs and profit as well as the relationship between sales volume and profit. The coefficient of determination test shows that the independent and moderating variables are able to explain 56.8% of the variation in company profit, while the remaining 43.2% is explained by other variables outside the research model. This study implies that companies need to focus more on increasing sales volume and improving operational efficiency to enhance profitability.

The management of ethical drug inventory is a crucial aspect of supporting the smooth delivery of healthcare services in hospitals. Improper management of ethical drugs can lead to stockouts or overstock, which results increased operational costs and the risk of drug expiration. This study aims to analyze the inventory control of ethical drugs at RSUD Inche Abdoel Moeis Samarinda using the Economic Order Quantity (EOQ), Safety Stock, and Reorder Point methods. The data used in this study are the consumption of ethical drugs during 2024, obtained from the hospital’s Pharmacy Installation. The data analysis method involves calculating ordering and holding costs, determining the optimal order quantity using EOQ, and calculating Safety Stock and ROP to maintain drug availability during lead time. The results of this study indicate that the application of the EOQ method provides a more optimal and efficient order quantity compared to previous policies. Furthermore, Safety Stock and ROP calculations assist the hospital in preventing ethical drug shortages and creating a more structured ordering schedule. Thus, the implementation of EOQ, Safety Stock and ROP methods can improve the efficiency of ethical drug inventory management while supporting the continuity of healthcare services at RSUD Inche Abdoel Moeis Samarinda.

 This study was conducted to evaluate the influence of Total Asset Turnover (TATO), Debt to Equity Ratio (DER), and Net Profit Margin (NPM) in predicting stock return fluctuations. The study focuses on retail sector issuers listed on the Indonesian Stock Exchange (IDX) between 2021 and 2024. Through the application of panel data regression analysis, the study determined that the Common Effects Model (CEM) is the most appropriate estimation method. This decision was made based on a series of tests including the Chow Test and the Lagrange Multiplier. Although classical assumption testing showed symptoms of heteroscedasticity, this problem was addressed using the EGLS (cross-sector weighting) Panel method to ensure the validity of the estimates. Based on partial testing, it is found that TATO and NPM variables have a positive and significant contribution to stock returns, while DER is found to have no significant effect. Collectively, all independent variables had a significant effect, with the Adjusted R-Square value reaching 27.80%. This indicates that for investors in the retail sector, profitability and operational efficiency are important indicators in making investment decisions.

Edentulism is a condition of total tooth loss that has a significant impact on the efficiency of mastics, phonetics, and facial aesthetics. This case report presents the rehabilitation of a 72-year-old female patient with a condition of total mandible edentulus using two standard-diameter implants that support the overdenture with a locator retention system. Treatment procedures include clinical evaluation, radiographic analysis, implant placement, prosthesis placement, as well as follow-up evaluation to assess function and comfort. The results of the treatment showed an increase in patient retention, stability, and comfort in daily activities. In addition, patients reported improved confidence and quality of life after the use of implant-based overdentures. These findings confirm that overdenture with mandibular implant support is a predictive, effective, and reliable rehabilitation method in treating total edentulism. The success of this case provides clinical evidence that implant-based approaches are able to overcome the limitations of conventional prostheses, as well as being a solution that supports the functional and psychosocial aspects of elderly patients. Thus, implant-based overdenture can be recommended as the primary therapeutic option in the rehabilitation of mandibular edentulism.

Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from significant energy wastage due to their inability to adapt to real-time environmental changes, leading to high operational costs. Although Proportional-Integral-Derivative (PID) controllers are widely used for their simplicity and reliability, they struggle to handle the complex dynamics of modern environments, requiring advanced optimization to enhance efficiency. This study aims to optimize PID controllers by integrating the Queen Honey Bee Migration (QHBM) algorithm to improve HVAC performance, energy efficiency, and adaptability. The research method employs an experimental approach that compares the performance of conventional PID controllers with PID controllers optimized using the QHBM algorithm under dynamic environmental conditions. The results show that the PID-QHBM system significantly outperforms the conventional PID system, achieving a rise time of 0.2649 seconds and a settling time of 1.6874 seconds with an almost negligible steady-state error of 9.4991e-08. Although it experiences a slight overshoot of 16.3810%, the system stabilizes quickly and maintains the target temperature efficiently. In contrast, the conventional PID controller exhibits slower response characteristics, with a rise time of 1.3730 seconds, a settling time of 2.5144 seconds, and a larger steady-state error of 0.0361. This study demonstrates that integrating the QHBM algorithm into PID controllers provides a more effective solution for real-time temperature control, offering substantial improvements in energy efficiency and system performance. The findings contribute to advancing intelligent HVAC control systems that can better adapt to environmental variations while minimizing operational costs.

Digital transformation has brought fundamental changes to the structure and dynamics of modern industrial economics. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and big data not only modify production and distribution processes but also revolutionize marketing strategies and patterns of industrial competition. This study is motivated by the need to understand how digital marketing transformation influences the development of competitive advantage through changes in digital market structure from an industrial economics perspective. In this context, digital marketing functions as a strategic instrument that integrates technology, data, and consumer behavior into market mechanisms. The analysis shows that digitalization creates a network-based market structure characterized by the concentration of economic power in major digital platforms and dominance in data control. This structure affects the intensity of competition, the direction of innovation, and patterns of industry differentiation. Digital marketing transformation enhances efficiency, expands market access, and lowers entry barriers for new players, yet it also creates competitive imbalances due to the dominance of large platforms.Through a digital Structure–Conduct–Performance (SCP) approach, the study finds that market structure acts as an intermediary variable that channels the impact of digitalization on competitive advantage. Digitalization significantly promotes industrial efficiency, innovation, and profitability. Proposed strategic solutions include strengthening digital literacy, developing adaptive regulations, and fostering cross-sector collaboration to create an inclusive, competitive, and sustainable digital industrial ecosystem

Background: Automatic segmentation of the thyroid gland in ultrasonography (USG) images using deep learning requires a user-friendly interface to support diagnostic and educational processes. Purpose: This study aims to develop and implement a Graphical User Interface (GUI) that integrates a deep learning U-Net model for interactive and efficient segmentation and visualization of thyroid USG images. Method: The development method employed the Rapid Application Development (RAD) approach using MATLAB programming language. The GUI is designed to load transverse and sagittal USG images, display automatic segmentation results, and calculate thyroid gland volume based on dimensions measured automatically from the segmentation output. Testing was conducted using USG image data from 15 volunteers, and GUI functionality was evaluated using black box testing. Result: The GUI successfully displayed USG images and segmentation results with a responsive 4-panel interface; zoom, pan, and image navigation features functioned well. Automatic segmentation occurred in real-time after image input, and volume measurement results appeared automatically. Black box testing evaluation showed all GUI features operated as expected. The average Dice Similarity Coefficient (DSC) of 0.91 indicates high performance of the U-Net model in thyroid segmentation, consistent with previous findings. Statistical testing confirmed no significant difference between volume measurements using the application and manual methods (p = 0.953). Conclusion: This GUI implementation facilitates users in performing deep learning-based segmentation and visualization of thyroid USG images, improving efficiency and accuracy in thyroid volume measurement. The GUI has potential applications in clinical practice and radiology education.

The implementation of predictive maintenance supported by SAP Plant Maintenance (SAP PM) at PT Xyz has proven to be effective in reducing machine downtime, lowering maintenance costs, and improving asset reliability. The integration of SAP PM with Industry 4.0 technologies such as IoT sensors, AI-based analytics, and real-time notification systems strengthens operational efficiency and ensures continuous performance. Empirical results show improvements in key performance indicators, including a 20-25% reduction in downtime, a 30% reduction in maintenance costs, an increase in asset availability to 97%, an MTBF extension of up to 511 hours, and an OEE rate of 92.1%. These findings highlight the strategic role of digital predictive maintenance in increasing competitiveness and supporting long-term sustainability in manufacturing operations.

Waste management remains a critical environmental issue due to the lack of public awareness in separating organic and inorganic waste, resulting in accumulation and environmental pollution This study aims to analyze and evaluate the development of automatic waste sorting systems based on proximity sensors with full-capacity notification using a Systematic Literature Review (SLR) approach.. The proposed system utilizes a combination of sensors, including proximity sensors for material identification and ultrasonic sensors for detecting object presence and bin capacity, integrated with a microcontroller for real-time processing. Additionally, the system is equipped with IoT-based monitoring that allows users to receive notifications when the waste bin reaches its capacity. The research method involves system design, hardware and software integration, and functional testing to evaluate system performance. The results indicate that the system is capable of sorting waste automatically with a high level of accuracy and responsiveness, while also providing real-time monitoring to support waste management operations. The implementation of this system can reduce manual intervention, increase operational efficiency, and promote better waste segregation practices. Furthermore, this study highlights the potential of integrating smart technology into environmental management systems, contributing both theoretically and practically to the development of sustainable waste management solutions.

The rapid development of information technology has had a significant impact on the world of education, particularly in the use of mobile applications as learning media. Developing Android-based applications using Flutter is one innovation that can increase efficiency and provide a more personalized and adaptive learning experience. In this context, educators are required to continue innovating to meet the increasingly diverse and dynamic needs of students in the digital era. The Multimedia and Information Technology Professional Association (PPMultindo), an organization that brings together technology professionals, is taking advantage of this opportunity by developing an Android-based learning platform. This platform is designed to provide easier, more flexible, and more interactive access to learning materials, as well as support the process of designing more engaging materials. Features such as learning data analysis, material recommendations, and automatic evaluation are expected to increase the effectiveness and efficiency of the teaching and learning process, while also providing a means for collaboration between members. However, the implementation of this technology still faces various challenges, such as limited technological competence of educators, inadequate infrastructure, and the need for curriculum adjustments. Therefore, collaborative efforts are needed to optimize the use of mobile technology to support educational transformation.

This study aims to evaluate the effectiveness of implementing an application-based water payment system as a strategy to reduce consumer arrears. Payment delinquency remains a significant challenge in the management of water utility services, often driven by low payment discipline, limited access to transaction channels, and the inefficiencies of conventional manual systems. Employing a quantitative approach, this research utilizes comparative data collected before and after the implementation of the digital payment application, supported by statistical analysis to measure changes in consumer payment compliance. The findings indicate that the adoption of an application-based payment system has a substantial positive impact on reducing arrears, as reflected in increased payment timeliness and a decline in the number of delinquent consumers. Features such as ease of access, automated notifications, and transparent billing information are proven to enhance consumer compliance behavior. These results underscore that the digitalization of payment systems can serve as an effective solution for improving operational efficiency and supporting the sustainability of water utility services.

Digital transformation in the public sector encourages civil servants to utilize technology not only for administrative purposes but also to improve efficiency, accuracy, and work performance. This study examines the influence of Intelligent Learning-Based Training and Digital Training Components on the Digital Productivity of civil servants at the Bureau of Economic Affairs of the Regional Secretariat of Lampung Province, with Adaptive Competence serving as a mediating variable. A quantitative approach with a non-experimental survey design was applied, and data were collected through structured questionnaires distributed to employees engaged in digital-based work activities. The data were analyzed using structural model testing procedures. The findings reveal that Intelligent Learning-Based Training does not significantly influence Adaptive Competence or Digital Productivity. Conversely, Digital Training Components positively and significantly affect both Adaptive Competence and Digital Productivity. Adaptive Competence also has a positive impact on Digital Productivity and mediates the relationship between Digital Training Components and Digital Productivity. However, Adaptive Competence does not mediate the relationship between Intelligent Learning-Based Training and Digital Productivity. These results indicate that civil servants’ digital productivity is more effectively improved through practical, relevant, accessible, and well-evaluated digital training programs than through intelligent learning systems alone. Therefore, public institutions are encouraged to develop digital training initiatives that align with employees’ daily work demands while strengthening their adaptive competence in responding to technological changes.

Strengthening the capacity of micro, small, and medium enterprises (MSMEs) is a strategic effort to enhance business competitiveness and sustainability, particularly within the Pamulang MSME Friends (SUP) community. This community service activity aims to improve MSMEs’ financial management capabilities and increase production efficiency through the development of a joint production house. The main challenges faced by partners include limited financial literacy, the absence of systematic financial transaction records, and inadequate production facilities. The implementation of the program was carried out through training activities, mentoring in the use of the APIK financial recording application, and facilitation in designing a collaborative production house model. The results indicate an improvement in MSMEs’ understanding of financial management, including the ability to record transactions systematically, separate business and personal finances, and calculate production costs more accurately. Furthermore, the establishment of a joint production house contributes to greater cost efficiency, increased production capacity, improved product quality, and stronger collaboration among MSME actors. Therefore, this activity contributes to creating a more structured, efficient, and sustainable MSME ecosystem that supports long-term business development.

The low thermal efficiency of Gas Power Plants (PLTG) due to exhaust gas heat loss drives the implementation of cogeneration at PLTGU Block II PT. PLN Indonesia Power UBP Semarang. This study analyzes the performance of the Gas Turbine Generator (GTG), combined cycle efficiency, and Exergy distribution using a 3-3-1 configuration. The research utilizes actual operational data from January 28, 2026, sampled at 10-minute intervals. Results indicate that cogeneration via a Heat Recovery Steam Generator (HRSG) significantly enhances plant efficiency. The GTG output ranged from 273–283 MW with an efficiency of 30.0–30.2%. Following combined cycle integration, system efficiency increased to 43.9–44.4%, a gain of approximately 14%, with a heat rate of 11,916–11,988 kJ/kWh. Exhaust heat of 665–713 MW was recovered to generate an additional 130 MW through the Steam Turbine Generator (STG). Exergy analysis reveals that the largest irreversibility occurs in the GTG combustion process (285 MW), followed by the HRSG (185 MW) and STG (49 MW).

This study aims to examine the integration of Generative AI in higher education through the perspective of the SAMR framework, which consists of Substitution, Augmentation, Modification, and Redefinition. The study used a narrative literature review method by analyzing 30 relevant academic publications published between 2023 and 2025. The findings reveal that the implementation of GenAI in higher education is still predominantly situated at the substitution and augmentation levels, with primary functions focused on improving efficiency, automation, and academic assistance. In contrast, transformative applications categorized under modification and redefinition remain relatively limited and are still in the early stages of development. This study concludes that although Generative AI possesses substantial potential to transform higher education practices, its current implementation has not yet reached an optimal transformative stage. Therefore, future educational practices should emphasize the development of more transformative implementation strategies that move beyond efficiency-oriented utilization toward fostering pedagogical innovation and meaningful learning transformation.