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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 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 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.

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.

Traffic performance on urban road segments is strongly affected by vehicle volume and travel time, particularly during peak periods. This study analyzes the relationship between travel duration and the total number of vehicles passing along Otto Iskandar Road as an illustration of urban traffic conditions. Data were collected through field surveys, focusing on two main variables: average vehicle travel time and total traffic volume. Statistical analysis was performed using IBM SPSS Statistics, including normality testing and the Wilcoxon Signed Rank Test to identify potential differences between the observed variables. The results show a difference in average values between travel duration and vehicle volume; however, this difference is not statistically significant at the 95% confidence level (p = 0.180). These findings indicate that increases in traffic volume do not always lead to proportional increases in travel time, although they can still influence the stability and efficiency of traffic flow. The results are consistent with previous studies, such as Halim (2021), who reported that U-turn movements affect speed and traffic performance, and Handayani et al. (2024), who found that parking activities and vehicle maneuvers reduce road capacity. Other studies also highlight the impact of side friction and traffic flow variations on speed and saturation levels. Overall, this study emphasizes the importance of managing vehicle flow and monitoring travel time in urban transportation planning and traffic management.

The integration of artificial intelligence (AI) in forensic investigation has significantly transformed the analysis and authentication of digital evidence. This paper explores the role of AI technologies, specifically machine learning and deep learning algorithms, in examining digital evidence from various sources, including computers, mobile devices, and network systems. We provide an in-depth analysis of current AI-based forensic tools, their efficiency in evidence authentication, and the challenges they face regarding legal admissibility. Our findings indicate that AI-powered forensic systems can detect digital evidence tampering with 94.7% accuracy, drastically reducing analysis time from weeks to hours. However, challenges remain, particularly in areas such as algorithmic transparency, bias prevention, and ensuring the integrity of the chain of custody. This research offers a framework for incorporating AI in forensic laboratories, while also addressing crucial legal and ethical concerns to ensure the admissibility of AI-analyzed evidence in court. These considerations are essential for the widespread acceptance and use of AI in forensic investigations.

The attendance recording system at PLN ULP Tanjungbalai still relies on manual, paper-based methods, resulting in delays in data recap, reduced efficiency, and a high potential for recording errors. This condition affects the accuracy of employee attendance information, which is essential for administrative activities and managerial decision-making. Based on these issues, this practical work aims to design and develop a web-based e-attendance application as a solution to enhance efficiency, processing speed, and the accuracy of attendance recapitulation. The system was developed using PHP as the programming language and MySQL as the database management system, following several stages including requirement analysis, system design using UML, and implementation of a web-based user interface. The application provides essential features such as user login, daily attendance recording, employee data management, attendance notes (permission, sickness, etc.), and automatic attendance report generation. The system is designed for two types of users—Admin and Employees—each with specific access rights. The implementation results indicate that the e-attendance application significantly improves the efficiency of attendance administration at PLN ULP Tanjungbalai. Data collection and recapitulation become faster, more structured, and less prone to errors, while also enabling administrators to monitor employee attendance in real time. Therefore, this web-based e-attendance application serves as an effective solution to support operational activities and enhance the quality of employee attendance management.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

Supplier selection is a strategic aspect of supply chain management that directly impacts product quality and operational efficiency within a company. PT. XYZ, operating in the fashion industry, is currently facing the challenge of selecting the optimal t-shirt supplier and lacks an appropriate decision-making method. The objective of this research is to provide insights into the best decision-making approach for PT. XYZ by applying the Analytical Hierarchy Process (AHP) method. The determination of suppliers is carried out by considering four main criteria, namely product quality, product price, delivery time, and service. The data obtained were generated from interviews and questionnaires given to the owner of PT. XYZ. The results of the analysis have shown that product quality is the most influential criterion with a weight of 0.716, followed by service (0.113), delivery time (0.093), and product price (0.078). The final result states that supplier C has the highest priority weight, which is 0.763. Therefore, PT. XYZ is recommended to choose supplier C as the best alternative supplier. The results of this study indicate that the AHP method can be used as a systematic and objective decision-making tool for supplier selection.

Queueing systems play an important role in evaluating service performance, especially in small-scale businesses such as barbershops, where fluctuating customer arrival patterns and limited service capacity often lead to long waiting times. This study aims to analyze the performance of barbershop services using the M/M/1 queueing model and an analytical approach based on experimentally tested arrival (λ) and service (μ) rates. The model was selected because it represents a single-server system with Poisson arrivals and exponentially distributed service times, closely matching real barbershop operational characteristics. Using assumed realistic parameters, the analysis shows that when λ = 12 customers per hour and μ = 6 customers per hour, the system becomes unstable with a utilization rate (ρ) exceeding 1, indicating continuous queue growth. Further simulations with increased service rates demonstrate significant improvements: at μ = 15, the system achieves ρ = 0.8 with an average waiting time of 16 minutes, while at μ = 13, the system remains stable but experiences a long waiting time of approximately 55 minutes. These findings emphasize that barbershop performance is highly sensitive to service speed and that even small increases in μ can produce substantial improvements in queue stability and customer waiting times. The study concludes that barbershops must ensure adequate service capacity—either through optimizing service duration, improving worker efficiency, or adding servers—to maintain service quality and enhance customer satisfaction.

Advances in digital technology have led to significant transformations in management accounting practices, particularly with the use of cloud accounting, big data analytics, artificial intelligence (AI), and digital-based management accounting information systems. These changes have resulted in a shift in the function of management accounting from merely a documentation tool to a strategic decision support system that provides information quickly, accurately, and in real time. This study aims to analyze the implementation of management accounting in the digital business era, identify the obstacles faced by organizations in the digitization process, and explain the opportunities that can be utilized to improve the efficiency of financial management systems. The research method applies a qualitative approach by conducting a literature study that reviews a number of journals, books, and scientific documents related to the topic. The research findings indicate that digitization has a positive impact on operational efficiency, clarity of information, and the quality of managerial decision-making. However, organizations still encounter various challenges, such as low human resource technological capabilities, complexity in system integration, and increased threats to data security. This study concludes that the implementation of digital management accounting is a strategic necessity for companies in the modern business era, requiring technological readiness, increased human resource capacity, and internal policies that support a complete digital transformation process.  

The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

Posyandu (Integrated Health Post) is a public health facility that plays a vital role in providing health services for toddlers and pregnant women. However, data management and reporting often face challenges, such as limited access to information and errors in data recording. Therefore, this study aims to develop a Web-Based Posyandu Payolaumbu Service Information System using the CodeIgniter Framework to improve efficiency and accuracy in data management and reporting. In the development phase, a system requirements analysis and web-based application architecture design were conducted. The system implementation uses the CodeIgniter Framework as a framework to produce a faster, more efficient, and more reliable application. Proposed features include recording health data for toddlers and pregnant women, immunization schedules, weighing, and health reports. The results show that the Web-Based Posyandu Payolaumbu Service Information System can improve efficiency in recording and reporting health data. Users, including posyandu officers, midwives, and administrators, can easily access and manipulate data in real-time. Furthermore, this system helps improve service quality by providing more accurate and complete information on toddler health. In conclusion, the implementation of the Web-Based Posyandu Payolaumbu Service Information System using the CodeIgniter Framework provides significant benefits for data management and health services at Posyandu Payolaumbu. Suggestions for further development include maximizing system utilization, developing additional features, routine maintenance, and ongoing evaluation based on user feedback. With these steps, it is hoped that this system can contribute more effectively to improving the quality of health services at Posyandu and supporting comprehensive public health efforts.

As a high-value crop, potatoes necessitate balanced nutrient management for optimal growth and yield. This research aimed to assess how varying applications of NPK 20-20-10 fertilizer influenced potato growth, yield, tuber quality, agronomic efficiency, and economic viability within tropical climates. The experimental setup involved a randomized complete block design, incorporating four replications across seven distinct treatments: a control, a standard inorganic fertilization regimen, and NPK 20-20-10 applied at 0.50, 0.75, 1.00, 1.25, and 1.50 times the suggested dosage. The findings indicated that applying NPK 20-20-10 significantly enhanced several parameters, including plant height, branch count, tuber count, tuber weight, and overall yield components, when contrasted with the control group. Notably, the 1.25 times recommended dose demonstrated superior performance, leading to a 34.9% increase in tuber number and a 68.6% rise in tuber weight compared to the control. Agronomic effectiveness scores surpassed 100 for dosages ranging from 0.75 to 1.50, with the 1.25 dose registering the peak value. Economic evaluations confirmed the profitability of all NPK treatments, and the 1.25 dose yielded the most favorable R/C ratio and a net profit of IDR 29,053,400. Consequently, the recommended application for potato cultivation is 675 kg/ha of NPK 20-20-10, distributed in three equal parts at planting, four weeks post-planting, and six weeks post-planting. Thus, these results underscore that NPK 20-20-10, when applied at 1.25 times the recommended rate, presents an agronomically effective and economically sound strategy for sustainable potato farming in tropical settings.

This study used a qualitative approach with a Systematic Literature Review (SLR) as the data collection technique. This study examined the institutional factors causing long dwelling times at Tanjung Emas Port and mitigation efforts. The analysis revealed that the main root of the problem lies in the lengthy administrative and goods inspection (customs) processes, particularly in the red, yellow, and green inspection lanes. Obstacles include the lack of data integration (such as PIB and SPPB dates) between the Semarang Container Terminal (TPKS) and Customs, as well as incomplete documents by service users. A significant impact was felt on imports, where dwelling times were longer due to complicated quarantine and customs inspections, while exports were relatively unaffected.

This study examines passenger service procedures on the KM Gunung Dempo at PT PELNI's Sorong Branch, specifically related to the efficiency of the embarkation and disembarkation processes. This study was motivated by operational constraints such as long queues, limited terminal facilities, and low staff productivity. Using a qualitative descriptive method with a quantitative approach, data was collected through observation, interviews, and documentation. Analysis was conducted by calculating time efficiency and service productivity. The results revealed a time efficiency level of 66.7% (categorized as inefficient), where the actual time reached 180 minutes from the standard of 120 minutes. In addition, staff productivity was only 0.73 passengers/minute, still below the ideal standard (≥ 1 passenger/minute). The main inhibiting factors include a lack of personnel, a manual ticket verification system, minimal terminal facilities, and weak inter-agency coordination.