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Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in formative assessments, emphasizing the importance of considering cultural diversity, institutional culture, and educators' beliefs. AI technologies, while powerful in automating grading and providing personalized assessments, often face limitations in addressing complex student responses that require human judgment. Furthermore, cultural factors, such as students' prior exposure to technology and different cultural attitudes towards AI, play a critical role in the acceptance and effectiveness of these tools. Organizational factors, including leadership support, digital literacy, and the readiness of institutions to adopt AI, are also key determinants in the successful implementation of AI systems in education. Teachers’ beliefs about assessment influence their acceptance and use of AI tools, highlighting the need for professional development and training to ensure that AI enhances pedagogical goals rather than replacing human expertise. The study concludes that the alignment of technology, culture, and assessment beliefs is essential for the effective use of AI driven formative assessments in educational settings. Recommendations for educational institutions include adopting a socio technical approach to AI integration, with a focus on providing resources, training, and fostering a culture of innovation. Future research directions should focus on expanding studies to diverse educational contexts, conducting longitudinal research on AI’s impact on learning outcomes, and exploring additional socio technical frameworks to guide AI adoption in education.

This community service program aims to optimize construction project planning through assistance in preparing a business feasibility study at PT Sahabat Anugrah Sejati. The construction sector is a strategic industry that requires effective resource management, proper risk mitigation, and accurate planning to ensure project success. Therefore, the program supported the partner in analyzing financial, managerial, market, and economic aspects prior to making investment decisions for the proposed project. The results show that the project is feasible based on the Net Present Value (NPV) and Internal Rate of Return (IRR) analyses. An efficient organizational structure, a competent management team, and strong supervision mechanisms further strengthen the project’s operational readiness. Increasing demand for construction services also enhances the company’s market prospects. However, several areas still require improvement, including heavy equipment utilization, material efficiency, and digital marketing strategies. This program provides strategic recommendations to help PT Sahabat Anugrah Sejati improve project feasibility, operational effectiveness, and long-term business sustainability.

This study discusses the relevance and role of hybrid learning strategies as a conceptual solution to the crisis of effectiveness in traditional fiqh learning in Islamic boarding schools in the digital age. The background of this study stems from the problem of declining efficiency and interactivity of classical learning methods such as sorogan and bandongan amid technological developments that demand pedagogical innovation. This study aims to analyze theoretically how the integration of traditional pesantren values and modern learning approaches can be applied without eliminating spiritual identity and scientific sanad. The method used is library research with a descriptive-analytical approach through a review of the latest scientific literature on Islamic education and hybrid learning. The results show that a hybrid learning model based on synchronous and asynchronous approaches can increase flexibility, effectiveness, and student participation in the learning process, while maintaining personal relationships with teachers. The theoretical implications enrich the concept of integrating Islamic education and technology, while the practical implications offer an adaptive curriculum model for Islamic boarding schools to respond to the challenges of the digital age without abandoning Islamic values.

The rapid growth of e-commerce has increased the strategic role of freight forwarding and delivery service companies in maintaining the efficiency of supply chains. However, the increasing complexity of delivery processes also raises various operational risks, such as package damage, loss, delivery delays, and distribution errors. This study aims to analyze the implementation of risk management in freight forwarding companies operating in Brebes Regency and to assess the effectiveness of the risk management practices applied. This research employs a descriptive qualitative approach, with data collected through in-depth interviews, observations, documentation, and questionnaires. Informants were selected using purposive sampling, involving company management, operational staff, couriers, and customer service personnel. Data analysis was conducted using the Miles and Huberman interactive model and supported by average risk score analysis. The results show that most aspects of risk management fall into the high and very good categories, with an overall average score of 3.89. This indicates that the company has implemented risk management effectively, particularly in incident evaluation, risk measurement, and cooperation with insurance providers. Nevertheless, improvements are still needed in reducing the frequency of operational risks and enhancing the use of digital technology for risk monitoring to ensure a more comprehensive and sustainable risk management system.

This study aims to analyze the role and effectiveness of Islamic monetary policy in Indonesia in facing digital transformation, particularly through the instruments of Sertifikat Bank Indonesia Syariah (SBIS) and Sukuk Bank Indonesia (SukBI). The digital transformation of the national financial system demands an adaptive monetary policy that remains grounded in the principles of maqashid shariah. In the context of Islamic economics, monetary policy not only functions to regulate the money supply and maintain price stability but also ensures the realization of justice and economic welfare. This research employs a descriptive qualitative approach, using literature-based data collection from official publications of Bank Indonesia, the Financial Services Authority (OJK), and relevant academic references on Islamic monetary policy. The analysis adopts an inductive approach by examining the roles of SBIS and Sukuk BI in supporting the stability of the Islamic financial system and their alignment with maqashid shariah values such as al-‘adl (justice), al-wudhuh (transparency), and ar-rawaj (circulation of wealth). The findings indicate that digitalization has positively impacted the efficiency and transparency of Islamic monetary instruments, where SBIS plays a role in regulating the liquidity of Islamic banks in a non-usurious manner, while Sukuk BI serves as an essential instrument in maintaining national economic stability. Nevertheless, challenges remain, including the limited digital infrastructure for Islamic finance and the need to strengthen regulations to ensure that digital monetary systems remain consistent with sharia principles.

The development of artificial intelligence has revolutionized higher education by increasing the efficiency and personalization of learning, but it has also posed a major challenge in the form of a decline in students' critical thinking skills due to their dependence on technology. Therefore, an in-depth philosophical study through the post-positivism, critical theory, and constructivism paradigms is needed to understand its impact on cognitive and epistemological processes. This study aims to analyze how these three paradigms guide the development of critical thinking in the context of AI. It uses a descriptive-analytical method based on secondary data from academic literature such as journals and books that have been critically synthesized. The findings show that post-positivism views AI as a tentative critical realism tool for empirical verification. Critical theory critiques power relations, ideology, and the ethics of technological domination. Constructivism emphasizes the construction of social knowledge through human-centered design that supports creativity and collaboration. The positive impacts of AI include instant feedback and content adaptation, while the negative impacts include social isolation, weak digital literacy, and data privacy. Therefore, the implication is the wise use of AI with the assistance of teaching staff to strengthen reflective analysis, ethical literacy, and paradigm adjustments in accordance with the exact or social sciences to create multidimensional learning in the digital era.

Government expenditure is a great reason in economic stability and its impact on the balance of payments is dire. In this light, this paper seeks to use the time series analysis method and the ARDL model to investigate the association between the balance of payments of Iraq and the public spending within the 2004-2023 period. The Eviews 13 software was used to analyse it. The findings show that there is a positive association between spending by the people and balance of payment especially at the short run. The latter findings indicate that the efficiency of government expenditure reform is a necessary tool to accomplish the expansion and close the balance of payments deficit. This study highlights the importance of strategic fiscal policies and government spending in achieving a balanced economy and sustainable growth. Additionally, it emphasizes the need for continuous monitoring and adjustment of public spending to ensure its alignment with national economic objectives. The findings contribute to the understanding of fiscal policy implications in developing economies, especially in the context of Iraq’s economic challenges.  

In the current era of digital transformation, organizations are increasingly turning to Artificial Intelligence (AI) to enhance the precision and agility of managerial decisions. This study investigates how AI adoption within management accounting systems influences decision-making efficiency and managerial performance. Using survey data collected from managers and accountants in medium-to-large firms across Central Java, the research applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the hypothesized relationships. The results reveal that AI adoption significantly improves decision-making efficiency, which in turn enhances managerial performance. Furthermore, leadership support strengthens the relationship between AI adoption and decision-making efficiency, indicating that human and organizational factors remain critical for realizing technological benefits. The findings contribute to management accounting literature by elucidating the mechanism through which AI technologies create managerial value specifically, by accelerating analytical processes and improving the quality of managerial judgments. Practically, the study suggests that firms should focus on leadership involvement, data governance, and employee training to ensure that AI systems complement, rather than replace, human judgment. Overall, this study highlights that successful AI adoption in management accounting is not merely a technical transition but a strategic and cultural evolution toward evidence-based decision-making.

This study aims to deeply analyze the contribution of work motivation and work environment conditions in improving employee satisfaction and performance. The methodology applied is a Systematic Literature Review (SLR) following the guidelines of Wahono (2015) and Kitchenham (cited in Fauzi et al., 2018). Data were collected through literature exploration in Google Scholar, Garuda Ristekdikti, and university journal websites with a publication limit between 2020 and 2025. Through a selection process, a number of relevant articles were successfully collected for narrative analysis. The results of the study revealed that work motivation has a positive and significant influence on employee performance, both directly and indirectly through job satisfaction as a mediator. A supportive work environment also plays a crucial role in increasing employee enthusiasm, productivity, and job satisfaction. Overall, the synergy between high work motivation and a conducive work environment can create maximum performance and strengthen employee loyalty to the organization. The results of this study provide importance for human resource managers to pay more attention to motivation and work conditions as an approach to improving performance and efficiency in organizations.

In the era of globalization and digital transformation, organizations strive to maintain competitiveness through optimizing internal factors such as work culture, business process engineering (BPR), and decision support systems (DSS). This qualitative research based on a Systematic Literature Review (SLR) analyzes the relationship between these three using the Denison Organizational Culture Model, Hammer & Champy BPR, and the Resource-Based View (RBV). It finds that work culture mediates the implementation of BPR and DSS to increase efficiency by 30-50%, employee productivity, and sustainable competitive advantage. Case studies such as the Toyota Production System (TPS) confirm this synergy, resulting in reduced costs, cycle times, and improved service quality. This research also emphasizes the importance of a strong work culture in supporting the implementation of new technologies and methodologies, which in turn strengthens the company's market position. Thus, organizations that are able to integrate these three elements will be better prepared to face the challenges of globalization and dynamic changes in their industry, creating a sustainable advantage and being able to survive in a highly competitive market.

This study analyzes the effectiveness of using BNI Bank’s QRIS in improving transaction convenience and efficiency at the canteen of Universitas 17 Agustus 1945 (UNTAG) Surabaya. The research was conducted by a team led by Ida Ayu Nuh Kartini, S.E., M.M., together with students of the Management Study Program, during November–December 2025. The rapid development of digital banking has encouraged the adoption of QRIS as a standardized non-cash payment system regulated by Bank Indonesia. This study aims to examine the level of QRIS BNI utilization, assess ease of use (application access and QR code scanning), and evaluate transaction efficiency, including time savings, queue reduction, and error minimization. A descriptive quantitative approach was employed using a survey method with a five-point Likert scale questionnaire distributed to 36 respondents consisting of students, lecturers, and administrative staff who had used QRIS at the canteen. Primary data were analyzed descriptively using averages and percentages, supported by relevant literature. The results show that QRIS is perceived as highly effective, with scores above 90% for ease of use (95%), transaction speed (92.78%), reduction of change-related issues (97.78%), support for digitalization (97.22%), and security (91.11%). The main limitation identified is dependence on internet connectivity (85%). Overall, respondents strongly support the full implementation of non-cash payment systems at the campus canteen.

The objective is to analyze the competitiveness of modern retail through the integration of SWOT analysis as a basis for formulating more targeted strategies using qualitative descriptive methods. This paper identifies the strengths, weaknesses, opportunities, and threats that drive the performance of modern retail based on literature and empirical findings from various previous studies. The results of the analysis show that the main strengths of modern retail lie in product completeness, competitive prices, strategic locations, and operational efficiency. Meanwhile, weaknesses arise from suboptimal digitization, minimal online promotion, limited service innovation, and outdated inventory management. Meanwhile, opportunities arise from changes in digital-based shopping behavior, while threats emerge from the dominance of large retailers, e-commerce competition, and economic fluctuations. Based on the SWOT integration, a strategy is formulated that includes strengthening digitalization, optimizing online marketing, improving service quality, and modernizing operational systems to support long-term competitiveness.

The development of the digital era has triggered a significant transformation in consumer shopping patterns, which have now shifted from conventional retail to Quick Commerce (Q-Commerce). This article analyzes the phenomenon of changing consumer behavior driven by preferences for speed, practicality, and time efficiency, with the COVID-19 pandemic as the main catalyst. The study highlights that the success of Q-Commerce is highly dependent on Logistics Service Quality (LSQ), particularly in terms of timeliness, courier interaction quality, and order condition. Despite offering convenience that disrupts physical retail, this business model faces serious sustainability challenges, including high last-mile operational costs, difficulty achieving profitability leading to the closure of several market players, and intense competition from hybrid retail models. In addition, traffic safety issues and increased carbon emissions are highlighted as social and environmental impacts. This study concludes that while Q-Commerce holds great potential, its sustainability requires strategic innovations that balance service speed with cost efficiency and ecological responsibility.vThe development of the digital era has triggered a significant transformation in consumer shopping patterns, which have now shifted from conventional retail to Quick Commerce (Q-Commerce). This article analyzes the phenomenon of changing consumer behavior driven by preferences for speed, practicality, and time efficiency, with the COVID-19 pandemic as the main catalyst. The study highlights that the success of Q-Commerce is highly dependent on Logistics Service Quality (LSQ), particularly in terms of timeliness, courier interaction quality, and order condition. Despite offering convenience that disrupts physical retail, this business model faces serious sustainability challenges, including high last-mile operational costs, difficulty achieving profitability leading to the closure of several market players, and intense competition from hybrid retail models. In addition, traffic safety issues and increased carbon emissions are highlighted as social and environmental impacts. This study concludes that while Q-Commerce holds great potential, its sustainability requires strategic innovations that balance service speed with cost efficiency and ecological responsibility.

Industrialization is an economic development process characterized by the growth of the industrial sector as a key driver of economic progress in Indonesia. Beyond its role in creating added value and enhancing national competitiveness, industrialization is closely interconnected with other strategic sectors, including agriculture, services, infrastructure, and employment. Strong linkages between industry and these sectors are essential to ensure that economic growth is inclusive and sustainable. This study aims to analyze industrialization strategies that integrate and strengthen relationships with other sectors in the development process. The research employs a literature review method using a qualitative descriptive approach. The data are derived from secondary sources, including scientific journals, research articles, policy reports, and official publications relevant to industrial and sectoral development. The findings indicate that industrialization strategies aligned with agriculture can increase productivity and value-added processing, while linkages with the service sector and infrastructure development can improve efficiency, distribution networks, and market access. Furthermore, integrated industrialization contributes significantly to employment creation and regional development, reducing economic disparities between areas. Therefore, the formulation of an industrialization strategy that is well-coordinated with other sectors is crucial to achieving balanced economic growth, strengthening structural transformation, and supporting sustainable development in Indonesia.

This study aims to analyze the factors determining the growth of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia through a literature review approach. MSMEs make a significant contribution to the national economy, but their growth still faces various structural barriers. Based on the results of the literature review, it was found that access to capital, human resource quality, managerial capacity, innovation, digitalization, marketing strategies, and government policies are the main factors influencing MSME growth. Limited capital and low financial literacy are dominant obstacles that hinder business expansion. Furthermore, weak managerial capacity and digital competency prevent many MSMEs from optimally exploiting market opportunities. Innovation and the use of digital technology have been proven to increase efficiency and marketing reach, but the adoption rate remains low. External factors such as government regulations, simplified licensing, and support from empowerment programs also play a significant role in creating a conducive business climate. This study emphasizes that MSME development requires a comprehensive approach that integrates internal aspects of business actors and structural government support. The research results are expected to serve as a reference for academics, policymakers, and MSME actors in formulating more effective development strategies.

The rapid advancement of information technology has encouraged business actors to adopt digital transformation; this situation is also experienced by Pabrik Tahu Macanan, a small scale tofu factory in Magelang that still relies on manual systems in operation. This  study aims to analyze the implementation of management information systems in supporting digital transformation and risk management at Pabrik Tahu Macanan; a descriptive qualitative approach was applied, using interviews, observations, and documentation as date collection methods. The findings reveal that digital information systems have the potential to improve efficiency, recording accuracy, and internal control; however, their implementation remains limited due to human resource constraints and low adaptability to new technologies. The research also found that simple risk management practices such as regular machine maintenance and manual bookkeeping remain effective in maintaining business stability. The implication of this study indicates that a gradual implementation of digital based information systems, supported by training and supervision, can serve as a strategic step to enhance competitiveness, operational efficiency, and sustainability for traditional SMEs like Pabrik Tahu Macanan.