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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.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

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.

This study examines the implementation of Good Governance principles in passport and residence permit services at Ngurah Rai Immigration Office, Bali. Employing a qualitative case study approach, the research evaluates how digital transformation enhances transparency, accountability, efficiency, participation, and the rule of law in immigration services. Findings demonstrate that digital systems such as the M-Paspor application and the Integrated Residence Permit System have improved procedural transparency, reduced illegal levies, and strengthened public trust. Efficiency has increased through automation, which reduces processing times and human errors. Digital audit trails reinforce accountability by ensuring decisions are traceable and regulation-based. However, challenges exist in substantive accountability and responsiveness. Decision-making in non-standard cases lacks transparency, with limited explanation of the legal bases. Response times through formal channels such as hotlines often exceed standards due to lengthy cross-divisional coordination. Whilst digitalisation has brought significant improvements, further reforms are necessary to strengthen substantive accountability and responsiveness, thereby enhancing public trust and institutional legitimacy in accordance with good governance standards. Continuous improvements in integrated digital systems, staff training, and process streamlining remain essential for full compliance.

This study discusses the design and development of an automatic safety system for a wood cutting machine using Arduino Uno, a PIR (Passive Infrared) sensor, and a servo motor as the main components. The system is designed to automatically stop the movement of the wood cutting machine when human motion is detected around the cutting area, thereby minimizing the risk of work-related accidents. The research method includes hardware design, microcontroller programming, and system response testing using two types of test objects: the human body and a wooden block. The results show that the system operates according to the programmed logic. When the PIR sensor detects human motion, the servo motor stops and the red LED lights up as a danger indicator. In contrast, when no human motion is detected, the servo motor continues to move normally and the green LED remains on as a safe indicator. The system’s average response time is 0.6 seconds, indicating a fast and accurate performance. Therefore, the designed system is considered effective and can serve as a prototype of a simple safety tool to enhance operator safety in wood cutting machines.

Response time refers to the speed of nurses in providing initial responses to patients, measured from the moment patients arrive at the hospital entrance until they receive medical attention from emergency room staff. Patient satisfaction is an important indicator of healthcare quality, reflecting the comparison between patient expectations and the services received. This study passed the ethical review process with approval number 66/EC-LPPM/UWHS/IV-2025 and used a quantitative correlational design with a cross-sectional approach. The sampling technique applied purposive sampling involving 197 respondents. Data were collected using a nurse response time observation sheet and a patient satisfaction questionnaire that had undergone validity and reliability testing. The validity test using the Pearson Product Moment method showed that the calculated r value was greater than the r table value of 0.361, indicating that all questionnaire items were valid. The reliability test using Cronbach’s alpha produced a coefficient value of 0.855, confirming that the instrument was reliable. Data analysis used univariate and bivariate analysis with the Spearman’s rho test. The results showed that nurse response time was categorized as fast (≤ 5 minutes) for 177 respondents (89.8%). Patient satisfaction levels showed that 177 respondents (89.8%) were very satisfied, 10 respondents (5.1%) were satisfied, and 10 respondents (5.1%) were moderately satisfied. The Spearman Rank test produced a p-value of 0.000 (<0.05) with a correlation coefficient of 0.893, indicating a strong and positive relationship between nurse response time and patient satisfaction in the Emergency Room of Charlie Hospital Kendal.

Information technology (IT) has emerged as a critical component of public administration, offering significant potential to enhance transparency and accountability in governance. This comprehensive qualitative case study research examined how information technology implementation influences transparency and accountability mechanisms in public policy administration, while identifying critical challenges and success factors. The research employed embedded multi-case study methodology, collecting data through 118 semi-structured interviews with government officials (n=45), citizen-users (n=38), IT professionals (n=15), civil society representatives (n=12), and academic researchers (n=8), complemented by document analysis and direct observation (280 hours) across four case sites representing diverse governance contexts. Key findings demonstrate that information technology implementation significantly enhances government transparency through multiple mechanisms: public information portals increased citizen information access from 25-30% to 78-82%, mobile applications extended service accessibility from 15-20% to 42-55% in rural areas, and social media platforms reached 60-70% of citizens with policy information. Similarly, IT implementation strengthened accountability through online complaint systems that reduced government response times from 28-45 days to 5-12 days (60-75% improvement), automated audit systems that detected 35-55% more compliance violations, and real-time monitoring systems that reduced audit completion time by 40-50%.  The findings have implications for government practitioners seeking evidence-based guidance for IT implementation, policymakers developing governance policies leveraging technology, and academic researchers studying digital governance and public administration innovation.

Manual post-harvest paddy stirring requires significant time and labor and often results in uneven mixing, which can affect grain quality. To address this issue, this study designed and implemented a prototype of an Internet of Things (IoT)-based paddy stirring robot to simplify the process and improve efficiency. The system utilizes an ESP32 microcontroller as the main controller, DC motors as the stirring mechanism, and an IoT module for wireless connectivity to a mobile application. The research stages included hardware design, control system programming, IoT platform integration, and performance testing. Testing was conducted to evaluate response time, mixing uniformity, and power consumption. The results showed that the system could be operated remotely via a local Wi-Fi network with an average delay of less than 1 second, enabling real-time control. The prototype successfully stirred 0.3 kg of paddy with a mixing uniformity rate of 92% and an average power consumption of 12 watts. The application of IoT in the paddy stirring mechanism significantly improved time efficiency, reduced manual labor requirements, and maintained grain quality compared to traditional methods. These findings indicate the potential for further development into a large-scale automated paddy processing system with integrated humidity and temperature sensors for real-time quality monitoring, supporting the modernization of post-harvest processing through digital technology.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

This study presents the integration of a Large Language Model (LLM) Ollama with the OpenStreetMap (OSM) API within a Business Intelligence (BI) framework to develop an intelligent, location-based recommendation system. The system is designed to assist users in finding dining, leisure, and resting places through natural language interaction and contextual understanding. The LLM interprets user input semantically, transforms it into structured spatial queries, and retrieves relevant geospatial data from OSM. The data are then analyzed, categorized, and visualized using BI methods to enhance interpretability and decision-making. The system was implemented using Next.js, Leaflet.js, ensuring interactivity and scalability for web-based deployment. Technical evaluation focused on system accuracy, response time, and output consistency. Results demonstrate an average response time of 1.74 seconds, 80% accuracy, and 80% consistency, proving the model’s efficiency in producing relevant, context-aware recommendations. This integration highlights the potential of combining open geospatial data, local LLMs, and BI analytics to create intelligent, data-driven decision support systems applicable to tourism, urban planning, and spatial information management.

The limitation of physical memory (RAM) is a primary constraint hindering optimal performance in modern operating systems, especially when running large applications or performing intensive multitasking, often resulting in crashes and high latency. This research aims to quantitatively analyze the effectiveness of Virtual Memory (VM) implementation as a solution to this RAM constraint on the Windows 10 operating system, focusing on VM’s impact on CPU performance, GPU performance, and multitasking response. The methodology employed is a controlled experiment using industry-standard benchmarks: Cinebench R20 (CPU), Unigine Heaven (GPU), and response time measurements in intensive multitasking scenarios. Experimental results demonstrate that VM activation improves CPU/GPU performance by up to 5% and accelerates multitasking response time by up to 15%, confirming VM's effectiveness in mitigating memory bottlenecks. Nevertheless, this study also identifies potential performance overhead stemming from excessive paging and swapping processes, which trigger the phenomenon of Thrashing. Therefore, the research recommends a dual optimization strategy to achieve maximum and stable performance: software optimization via the Least Recently Used (LRU) algorithm to suppress page faults, supported by hardware optimization including the use of an SSD for the swap file and increased RAM capacity.

In the modern industrial world, robots like mechanical arms in automotive factories or packaging lines must move quickly and safely. For this, a real-time operating sistem (RTOS) is needed—think of it as a "super-fast brain" that ensures the robot reacts instantly to commands, without delays or errors. This paper analyzes popular RTOS like FreeRTOS and VxWorks for controlling industrial robots, focusing on evaluating performance (speed of operation) and stability (long-term reliability). We conducted tests in a lab using a simple robot that moves its arm to pick up objects. Performance was measured by response time (how quickly the robot stops when encountering an obstacle, ideally under 10 milliseconds) and throughput (how many tasks it can complete per second). Stability was checked through simulations of disruptions, like heavy loads or interfered sensor signals, using metrics such as error rate and time variation (jitter). Results show that FreeRTOS is more efficient for small, affordable robots, with high performance (average response time of 4 ms) but moderate stability (5-10% errors during overload). In contrast, VxWorks excels in stability (errors <2%, stable for up to 95% of tasks on time) for large factory robots, though it requires stronger hardware. Our analysis uses simple models like graphs and repeated tests, without complex formulas, to prove that the right RTOS can boost production efficiency by up to 25% and reduce accident risks.

Work safety is always a priority, especially in the mining world which has a high potential for danger. Therefore, there are many regulations that must be obeyed, including the prohibition of smoking in the cabin of the Dump Truck unit. This study aims to design a cigarette smoke detector in the cab of a dump truck as a form of warning to drivers who violate the ban on smoking in the cab. The method used in this research is a quantitative method by conducting experiments. The research conducted is to analyze the effect of smoke thickness levels (20%, 40%, and 60%) on different sensor types (MQ-4 and MQ-7). The expected result is the effect of smoke thickness variation on the response time of different sensors.

The purpose of this research is to analyze the innovation in emergency health services through the implementation of the Ambulans Hebat program in Semarang City, to analyze the challenges and obstacles faced in the implementation of the Ambulans Hebat service, and to analyze the quality of emergency health services provided by Ambulans Hebat. This research is descriptive qualitative in nature, using interview techniques with 9 informants and a questionnaire with 96 respondents. The results of this research show that the Ambulans Hebat program in Semarang City demonstrates innovation through digital technology (GPS, applications) that enhances response and coordination, an integrated command center for efficient information flow, and medical personnel training, although it requires better data integration, signal strengthening, and broader training; however, challenges such as limited fleet and medical personnel, weak inter-agency coordination, and traffic impacts necessitate additional units, integrated systems, and infrastructure innovations; quality evaluation indicates a 15-minute response time, high patient satisfaction, and successful initial handling, but requires route optimization, expanded satisfaction surveys, and additional technical training.

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.

Modern industries require automation to enhance operational efficiency and productivity. This study designs and implements an automatic sorting system based on the Internet of Things (IoT) using a mini conveyor, ESP32 microcontroller, TCS3200 color sensor, servo motor, and the Blynk application. The system is designed to accurately detect object colors, sort objects based on color, and enable remote monitoring and control via mobile devices. Testing results indicate that the system can detect and sort objects with a high level of accuracy, although some errors occur due to changes in lighting conditions. The conveyor speed is consistently recorded at 0.335 m/s, while the system's response time, including color detection, servo movement, and application updates, operates within an optimal range. The Blynk application enhances the flexibility of real-time system control. This study demonstrates that IoT-based sorting systems can serve as an efficient solution to support industrial automation. Further development is recommended to improve color detection accuracy and expand the system's application in more complex industrial scenarios.

The increasing integration and digitization of smart grid systems have exposed them to a variety of security threats, necessitating robust security measures to ensure their reliability and efficiency. This paper proposes a novel Digital Twin-Based Cyber-Physical Security Framework, incorporating AI-driven predictive maintenance and zero-trust architecture to address the evolving challenges of securing smart grids. By leveraging digital twin technology, this framework creates a real-time virtual representation of physical systems, enabling continuous monitoring and simulation for enhanced security and operational performance. Zero-trust security principles are integrated to ensure that no entity, whether inside or outside the network, is trusted by default, thus significantly reducing the risk of cyber-attacks. Additionally, AI-driven predictive maintenance enhances the framework’s reliability by proactively identifying potential failures before they occur, reducing downtime and improving system resilience. Through the development and simulation of this framework, including attack and failure scenarios, the paper demonstrates that the proposed system outperforms traditional methods in terms of anomaly detection, system downtime, and response times. The integration of predictive maintenance allows for early identification of component failures, thus enhancing the overall resilience of the grid. The zero-trust architecture further strengthens the cybersecurity posture, preventing unauthorized access and attacks. The study also identifies challenges, such as data synchronization and scalability, which must be addressed for broader implementation in large-scale smart grid systems. The findings suggest that the proposed framework could play a critical role in the future evolution of smart grid security, offering valuable insights for researchers and practitioners.  

The DH Building of the Electrical Engineering Study Program at Udayana University faces significant challenges in energy efficiency, as it still relies on conventional electrical systems. User negligence, such as forgetting to switch off lights and air conditioners (AC) after use, often results in unnecessary energy waste and increased operational costs. This issue highlights the urgent need for smart solutions capable of automating energy management, reducing waste caused by human error, and supporting the creation of a more efficient and sustainable campus environment. To address this problem, this study designs and implements a smart building system based on the Internet of Things (IoT). The system employs a NodeMCU ESP32 microcontroller as the main processing unit, integrated with a series of sensors including a DHT22 sensor for monitoring temperature and humidity, an MQ2 sensor for smoke detection, a PIR sensor for motion detection, and a PZEM-004T sensor for monitoring energy consumption. Control of electronic devices such as lights and AC units is carried out both automatically and manually through relay modules connected to the system. All sensor data and control functions are accessed via a web interface developed using the Laravel framework and a MySQL database. The testing results indicate that the designed system was successfully implemented and functions as expected. Sensor testing demonstrated high accuracy compared to standard measuring instruments, while the electronic device control system achieved an average response time of approximately 3.6 seconds, proving its reliability. Overall, the system provides a comprehensive solution for energy consumption monitoring and control, while also enhancing comfort and safety in the DH Building, in line with the goals of energy efficiency and facility modernization.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

The Student Evaluation of Lecturers (EDOM) at the Faculty of Engineering, Universitas Dr. Soetomo has historically been conducted via Google Forms and processed manually, resulting in slow response times, repetitive recap procedures, and insufficiently documented reporting validity. This study aims to design, build, and evaluate a web-based EDOM system using the Prototype Model (MODEL PROTOTYPE) to improve data collection efficiency, recap accuracy, and the effectiveness of feedback for teaching quality assurance. The development method comprises requirements elicitation with the Quality Assurance Unit through a review of SPMI documents and brief interviews, architectural and interface design (Laravel, MVC pattern, and MySQL), staged prototyping, and rapid iterations based on stakeholder feedback until functional conformity is achieved. The resulting system provides authentication and role-based authorization (Admin, GPM, Lecturer, Student), odd/even period management, a question bank employing a Likert scale, anonymous questionnaire submission, score recap per lecturer and per category (pedagogical, professional, personal, social), dashboard visualizations, and report export to support governance needs. Functional evaluation was conducted through black-box testing on core scenarios (login, period scheduling, submission, recap, and export) and indicated conformance to specifications. User acceptance evaluation employed the Technology Acceptance Model (TAM) to examine perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention; the results indicate positive acceptance and potential for operational adoption. Practically, the prototype approach accelerates requirement alignment, reduces the risk of mis-specification, and facilitates change control, while the Laravel-based implementation supports maintainability, role-based access security, and further feature development. The study is limited to a single faculty; future work may include SSO integration, audit trails, and longitudinal, cross-semester analytics for more comprehensive monitoring of lecturer performance.