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Computers in one of the network companies at PT. Estrada uses the Fortinet operating system. The final result expected through this implementation is to comprehensively see the capabilities of the firewall on Fortinet in overcoming the problem of blocking social media applications and streaming platforms during working hours. Blocking the application in question is the ability to filter web processes such as Facebook, Instagram, YouTube, etc. In the tests carried out, web filtering was able to block applications on social media and streaming platforms, which proves that the performance of web filtering is quite good. In analyzing web filtering performance, use the office hour rule tool by carrying out the rule schedule in the Fortinet network and displaying all the information in detail. The final result obtained in the network application filtering simulation process using Fortinet is that every network sent cannot be entered (blocked) on both social media applications and streaming platforms.

The Operational Aspect in Islam emphasizes that production activities must benefit humanity or be related to meeting human needs. For example, the selection of raw materials should not come from haram sources, the production process should be free from activities prohibited by Sharia, production should not be excessive, and there should be no waste. The Islamic view on human resources emphasizes that all workers are huan beings, not robots or business tools. A A Business Feasibility Study refers to an analysis of a business plan, both prior to its implementation and once the business is operating on a regular basis. The aim of this study is to evaluate the operational aspects and human resource management within the feasibility study of the Islamic-based convection business, Brand 57 Busana Pekalongan. This research employs a qualitative approach. The data were collected through field research, which involves conducting the study directly at the site where the phenomena related to the research problem occur. The techniques applied for data collection include observation, interviews, and documentation The result of this study indicate that the Brand 57 Busana pekalongan is feasible in terms of operational and human resource management aspects from a Sharia businesss feasibility study perspective because it has chosen a strategic location, good product quality, adequate production capacity, and technology utilization. In addition, the brand 57 Busana Pekalongan convection is deemed permissible for operational production activities from an Islamic perspective, such as production activities based on Islamic values and Maqashid Syariah. In the implementation of human resources management, the Brand 57 Busana Pekalongan convention has implemented job descriptions, a Muslim work ethic, a fair and decent salary distribution system.

Proprioceptive sensor data, including inertial measurement units (IMU), joint encoders, and torque sensors, plays a critical role in state estimation for quadruped robots operating in dynamic and unstructured environments. However, these signals are often degraded by various sources of error, such as high-frequency noise, bias, drift, and contact-induced disturbances, which directly affect estimation accuracy and stability. This study presents a systematic analysis of sensor-specific noise characteristics and evaluates the effectiveness of preprocessing methods tailored to each sensor modality. Specifically, moving average filtering is applied to encoder signals to mitigate noise amplification during differentiation, while first-order low-pass filtering is employed for IMU and torque signals to suppress high-frequency noise. Experimental results on a publicly available quadruped dataset demonstrate that encoder velocity RMSE is reduced by 12.09%, high-frequency energy decreases by 59.63%, and signal-to-noise ratio (SNR) improves by 145.6%. However, variance reductions remain limited (3.39% for IMU and 4.05% for torque), indicating the persistence of impulsive, non-Gaussian noise caused by contact events. These findings highlight that linear preprocessing methods are effective for attenuating high-frequency noise but insufficient for handling non-Gaussian disturbances. The study provides practical insights into the effectiveness and limitations of preprocessing strategies, serving as a foundation for developing more robust signal processing and state estimation frameworks in quadruped robotics.

Peningkatan kasus gangguan kardiovaskular menuntut adanya sistem pemantauan detak jantung yang tidak hanya akurat, tetapi juga mampu memberikan respons cepat terhadap kondisi abnormal. Penelitian ini bertujuan mengembangkan sistem pemantauan detak jantung berbasis Internet of Things (IoT) yang terintegrasi dengan algoritma Extreme Gradient Boosting (XGBoost) untuk mendeteksi pola detak jantung abnormal secara real-time serta memicu respons otomatis melalui Miniature Medical Car. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan metode simulasi, di mana data denyut jantung dikumpulkan menggunakan Pulse Armband berbasis ESP32-C3 Mini dan sensor MAX30102, kemudian dikirim ke server untuk diproses melalui pipeline data mining. Dataset penelitian terdiri dari 800 sampel yang dibagi menjadi kelas normal dan abnormal, kemudian diolah menjadi 14 fitur statistik dan variabilitas detak jantung untuk proses pelatihan model. Hasil penelitian menunjukkan bahwa model XGBoost mencapai akurasi 94%, precision 93,3%, recall 93,3%, F1-score 0,933, serta ROC-AUC 0,96, yang mengindikasikan kinerja tinggi dalam membedakan pola detak jantung normal dan abnormal. Integrasi model dengan miniature car memungkinkan sistem memberikan respons fisik secara otomatis saat mendeteksi kondisi berisiko. Kontribusi penelitian ini mencakup pengembangan sistem IoT–ML real-time yang efisien, penggunaan fitur statistik ringkas untuk data fisiologis, serta implementasi respons berbasis robotik. Implikasi penelitian membuka peluang penerapan pada sistem monitoring medis jarak jauh, lingkungan publik, maupun perangkat asistif berbiaya rendah.

The development of automation and robotics technology has driven innovation in various industrial fields, particularly in automatic sorting systems. Manual sorting processes often lead to inefficiencies and human errors, creating the need for an automatic, fast, and accurate system. This research employs a qualitative method which includes experimentation, testing, and system documentation. The system is designed as a robotic arm for sorting objects based on color, utilizing a TCS3200 color sensor and an ESP32 microcontroller. An ultrasonic sensor detects the presence of objects, while the sorting results are displayed through a real-time web monitoring system. The test results show that the prototype successfully sorts four primary colors (red, green, blue, and yellow) with a high level of accuracy. This research is expected to serve as a reference for the development of automation systems and robotics learning tools in both educational and industrial applications. In addition, this research also contributes to the development of technology that can increase efficiency and accuracy in industrial production processes and provide more environmentally friendly solutions by reducing the need for manual labor.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

Autonomous decision-making systems increasingly rely on complex artificial intelligence models to operate in dynamic and safety-critical environments. While these models provide strong predictive capabilities, their black-box nature limits transparency, trust, and accountability. This study proposes a structured research methodology for integrating Explainable Artificial Intelligence (XAI) into autonomous decision-making systems. The research adopts a conceptual–analytical approach to develop an explainability-oriented framework that embeds transparency across perception, decision-making, and action execution stages. The methodology includes literature-driven problem identification, conceptual framework construction, classification and mapping of XAI methods, and formulation of explainability evaluation criteria. The results demonstrate that effective explainability in autonomous systems requires a hybrid integration strategy, combining in-model transparency with post-hoc explanation mechanisms. A structured mapping of XAI techniques to autonomous system components and a conceptual decision-flow diagram are presented to illustrate explainability integration. The findings highlight that layered and context-aware explainability enhances system interpretability, supports human oversight, and improves safety relevance without compromising autonomous operation. This study contributes a reusable methodological foundation for the design and evaluation of explainable autonomous systems, offering practical guidance for future empirical validation and real-world deployment in safety-critical applications.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

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.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

The rapid development of digitalization and innovation has become a key driver in improving business processes and the competitiveness of organizations worldwide. This study is the first comprehensive bibliometric analysis examining the relationship between digitalization and innovation in business processes, to map the intellectual structure of this field, track the development of its themes, and identify remaining research gaps. This analysis, which utilizes data from Scopus processed using VOSviewer and Biblioshiny software, covers publications from 2010 to 2024 and employs co-occurrence, co-authorship, and thematic evolution techniques. The results show a rapid growth in publications since 2016, peaking at over 110 publications in 2024. Eight key thematic clusters stand out: Industry 4.0, artificial intelligence, robotic process automation, blockchain, drivers, and agile business process management. Despite the field's maturity, it still suffers from high fragmentation, strong geographic concentration, and a reliance on cross-sectoral research designs. As a result, longitudinal insights remain limited, and digital transformation failure rates remain high, reaching up to 70%. This research presents the first quantitative and visual roadmap of global knowledge flows in this domain and underscores the need for longitudinal, geographically inclusive, and people-centric research to move beyond single-point understandings to a sustainable, context-sensitive framework that enhances both the theoretical depth and practical success of digital-based business process innovation

In recent years, with the rapid development of artificial intelligence, many innovative changes have been made in the field of intelligent mobile robot development. In the field of control and navigation of mobile robots, learning-based methods have many advantages over traditional ones. The study of mobile robot control methods using deep reinforcement learning is a remarkable area in the development of mobile robots that must operate in dynamic environments. In the previous studies, the proposed robot control algorithms using deep reinforcement learning are mostly based on the given target point and obstacle information, the robot path planning is performed, and the corresponding control is based on the obtained path. The DDPG-based method is a typical example. However, in dynamic environments, DRL based robot path planning requires a state of target point and obstacles information, which leads to a large amount of computation, resulting in extremely long convergence time and even non-convergent cases. In this paper, we propose a new method for mobile robot control in dynamic environment that solves the dimensional problem by extracting the features of the configuration of obstacles using autoencoder and learning the DDPG algorithm based on the obtained features. Simulation results show that the proposed algorithm can effectively solve the mobile robot control problem in dynamic environment.

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

Robotic extracurricular activities have the potential to develop students' creativity. The implementation of robotics extracurricular programs in improving students' creativity is an activity or activity that is determined outside of class hours, usually aimed at expanding students' insight or knowledge and skills related to technology, operation or development of human machines and human machine systems. The purpose of this study is to describe the design, process, and assessment of robotics extracurricular programs in improving students' creativity at MIN 2 Kediri. This research is a qualitative research with a case study research type. Data collection techniques use interviews, observation, and documentation. Meanwhile, for data analysis, researchers perform data reduction, data presentation, and drawing conclusions. This study produces findings that the robotics extracurricular program in improving students' creativity at MIN 2 Kediri has been running effectively. The methods used in the robotics extracurricular program are the PjBL method, STEM, demonstration, and practice.

In facing the challenges of the dichotomy of knowledge in the modern era, madrasas as Islamic educational institutions need to adjust the curriculum design that is relevant to the changing times, one form of development is by implementing an international standard madrasa curriculum. The purpose of this study is to reveal the strategies, challenges, obstacles and implications in implementing the development of a madrasa curriculum with international standards at MTs Tahfidz Yanbu'ul Qur'an Menawan Kudus. This study applies a Qualitative approach that is a case study, with data collection carried out through interviews, observations, and related documents. The findings of this study indicate that the method of implementing the curriculum at MTs Tahfidz Yanbu'ul Qur'an Menawan Kudus is formed from the integration of the Foundation curriculum, the Ministry of Religion, and the Innovative Curriculum which is then accumulated into intracurricular and extracurricular activities. The challenges and obstacles are caused by limited human resources, both teachers and student abilities. The implications for students are that it is able to produce students who have memorized the Qur'an and excel in the fields of foreign languages, research, science and technology, as shown by increased achievements in olympiads, science and robotics competitions.  

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.

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.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.