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This study aims to design and develop a waste complaint information system at the Environmental Agency of West Sumba Regency to improve the effectiveness of public services. The current problem lies in the manual complaint process, which leads to delays in handling reports, poor data documentation, and limited service transparency. This research employs a qualitative descriptive approach with data collection techniques including observation, interviews, and documentation studies. The system development adopts the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system is modeled using Unified Modeling Language (UML), including use case diagrams, activity diagrams, and sequence diagrams to provide a structured representation of the system. This approach is considered effective as it ensures a systematic and well-organized development process. The results indicate that the developed system facilitates the public in submitting complaints online and assists the agency in managing complaint data in an integrated manner. Furthermore, the system enhances response time, transparency, and service efficiency. Therefore, this waste complaint information system can serve as a technological solution to improve the quality of public services.

This research aims to analyze the implementation of the "Jalak Wadul Mas" (Jawa Timur Layanan Pengaduan Warga dan Dukungan Masyarakat/East Java Citizen Complaint Service and Community Support) innovation program in improving the welfare of people with social welfare problems (PMKS) in East Java Province. The Social Service of East Java Province developed this program as an integrated digital platform for complaint handling, social assistance distribution, and empowerment of vulnerable groups. Using the policy implementation theory from Edward III, this study examines four critical factors: communication, resources, disposition, and bureaucratic structure. This qualitative research employs a descriptive approach, with data collected through in-depth interviews, observation, and documentation at the Social Service of East Java Province during June-August 2025. Informants include program managers, field social workers, PMKS beneficiaries, and community stakeholders. The results indicate that the Jalak Wadul Mas program has successfully served 45,678 PMKS across 38 districts/cities in East Java, with a 78% complaint resolution rate and average response time of 3 working days. The program integrates multiple services, including emergency assistance, rehabilitation referrals, skills training, and economic empowerment. Key success factors include strong leadership commitment, adequate technology infrastructure, and collaborative networks with community organizations. Challenges remain in human resource capacity, internet connectivity in remote areas, and cross-sectoral coordination. This study recommends strengthening digital literacy training for beneficiaries, expanding mobile service units, developing real-time monitoring dashboards, and establishing sustainable funding mechanisms.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

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.