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This study investigates the performance of Internet of Things (IoT)-based monitoring systems using a mobile hotspot and IoT sensors for temperature and humidity data transmission. The research is based on the IoT concept, which enables electronic devices to communicate and exchange data through internet networks without direct human intervention. System performance was evaluated using standard Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. The experimental setup utilized a NodeMCU ESP32 microcontroller and a DHT22 sensor, with measurements conducted at various transmission distances through wireless communication media. The objective was to determine the reliability of hotspot connectivity and sensor communication in supporting IoT applications. The results indicate that the optimal performance was achieved at a distance of 20 meters using a 40-lambda variation. Furthermore, the communication signal between the ESP32 device and the mobile hotspot remained detectable up to a maximum distance of 32 meters. These findings demonstrate the effectiveness of the proposed IoT system for environmental monitoring applications within specific transmission ranges.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

One of the goals of a building is to create a comfortable environment that does not affect the health and operations of its occupants, therefore a system needs to be created to ensure comfort in classrooms. To fulfill a comfortable situation, there is a standard that regulates comfort, especially thermal and visual comfort. Thermal comfort is regulated in SNI 03-6572-2001 and visual comfort is regulated in SNI 03-6575-2001. The aim of this research is to design a tool to automatically monitor temperature and lighting, determine greater accuracy, determine temperature and lighting comfort distances, and test Smart Comfort measurement results in accordance with the SNI-03-6571-2001 and SNI-03-6575-2001 conformity standards. This design uses ESP32 with IoT-based LDR and DHT11 sensors which can be seen on the web and application, determines the accuracy and range of Smart Comfort values for monitoring temperature and lighting and determines the suitability of measurement quantities in the SDN PINANG 3 classroom.

National milkfish aquaculture production in 2024 reached 792,864 tons, highlighting the strategic role of this commodity in supporting the economy of coastal communities. central java,as one of the main contributors, has significant potential for pond development, including in Ujungwatu Village, Jepara Regency. However, pond management still faces challenges regarding human resource (HR) quality and the optimal utilization of technology. This community service program aims to enhance students’ capacity through outreach and training on Internet of Things (IoT)-based milkfish pond development as an effort to strengthen HR at the Sidomaju 2 SME. Implementation methods include the stages of observation and problem identification, work program planning, WebGIS design, IoT outreach and implementation, and evaluation. The materials covered included an introduction to IoT concepts based on ESP8266, pond monitoring, milkfish feed management, and the implementation of WebGIS as a digital mapping system for the Kalingga milkfish ponds.The activity was attended by 20 students from MA NU Ujungwatu and was conducted in a participatory manner through experiential learning. The results of the activity demonstrated an increase in students’ understanding of the application of technology in milkfish farming, as well as heightened awareness of the importance of efficient and sustainable pond management. The main output of the program is the Kalingga Milkfish Pond WebGIS, which can be utilized as a digital medium for education and monitoring of pond areas. This program contributes to improving students’ technological literacy while supporting the strengthening of pond operational systems based on digital innovation.

The implementation of smoking prohibition policies in Islamic boarding schools continues to depend largely on manual monitoring methods, which often face challenges related to consistency and supervision range. This study aims to design an Internet of Things (IoT)-based cigarette smoke detection system as an alternative monitoring approach that is more effective, measurable, and sustainable. The system design combines an MQ-2 gas sensor with a NodeMCU ESP8266 microcontroller programmed through the Arduino IDE platform. When smoke levels detected by the sensor exceed the predetermined limit, the system automatically triggers a buzzer and LED as warning indicators while simultaneously sending monitoring data to cloud-based platforms such as Firebase or ThingSpeak for real-time observation through web interfaces. The research outputs include a comprehensive system design consisting of system architecture, electronic circuit schematics, flowcharts, and pseudocode that are systematically arranged to support future prototype development and implementation. Through this design, the proposed system is expected to provide an initial technological solution that can enhance the effectiveness of monitoring and enforcing smoke-free regulations within Islamic boarding school environments.

The increasing demand for electrical energy and the limited availability of fossil fuels have driven the development of renewable energy sources, including marine current energy, which remains underutilized in coastal and remote maritime regions. This study presents the design and realization of a small-scale marine current power generation prototype using a horizontal axis propeller turbine with a NACA S814 blade profile and analyzes the effect of turbine rotational speed on electrical power output. The system converts marine current kinetic energy into mechanical energy through turbine rotation and subsequently into DC electrical energy using a generator, which is stabilized by a Buck–Boost Converter and Maximum Power Point Tracking (MPPT) for charging a 12 VDC battery. Real-time monitoring of electrical and mechanical parameters is implemented using an Internet of Things (IoT)–based system comprising an ESP32 microcontroller, a PZEM-017 sensor, and an RPM sensor. Experimental results demonstrate a positive correlation between water flow rate, turbine rotational speed, and generator output voltage. The system begins operating at a minimum flow rate of 35.2 L/s at 56 RPM, producing 0.2 V, while optimal performance is achieved at 45.3 L/s and 516 RPM, generating up to 13.3 V. These results indicate that the proposed prototype is a viable alternative renewable energy source for marine applications.

Digital transformation has brought fundamental changes to the structure and dynamics of modern industrial economics. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and big data not only modify production and distribution processes but also revolutionize marketing strategies and patterns of industrial competition. This study is motivated by the need to understand how digital marketing transformation influences the development of competitive advantage through changes in digital market structure from an industrial economics perspective. In this context, digital marketing functions as a strategic instrument that integrates technology, data, and consumer behavior into market mechanisms. The analysis shows that digitalization creates a network-based market structure characterized by the concentration of economic power in major digital platforms and dominance in data control. This structure affects the intensity of competition, the direction of innovation, and patterns of industry differentiation. Digital marketing transformation enhances efficiency, expands market access, and lowers entry barriers for new players, yet it also creates competitive imbalances due to the dominance of large platforms.Through a digital Structure–Conduct–Performance (SCP) approach, the study finds that market structure acts as an intermediary variable that channels the impact of digitalization on competitive advantage. Digitalization significantly promotes industrial efficiency, innovation, and profitability. Proposed strategic solutions include strengthening digital literacy, developing adaptive regulations, and fostering cross-sector collaboration to create an inclusive, competitive, and sustainable digital industrial ecosystem

Waste management remains a critical environmental issue due to the lack of public awareness in separating organic and inorganic waste, resulting in accumulation and environmental pollution This study aims to analyze and evaluate the development of automatic waste sorting systems based on proximity sensors with full-capacity notification using a Systematic Literature Review (SLR) approach.. The proposed system utilizes a combination of sensors, including proximity sensors for material identification and ultrasonic sensors for detecting object presence and bin capacity, integrated with a microcontroller for real-time processing. Additionally, the system is equipped with IoT-based monitoring that allows users to receive notifications when the waste bin reaches its capacity. The research method involves system design, hardware and software integration, and functional testing to evaluate system performance. The results indicate that the system is capable of sorting waste automatically with a high level of accuracy and responsiveness, while also providing real-time monitoring to support waste management operations. The implementation of this system can reduce manual intervention, increase operational efficiency, and promote better waste segregation practices. Furthermore, this study highlights the potential of integrating smart technology into environmental management systems, contributing both theoretically and practically to the development of sustainable waste management solutions.

The rapid growth of the Internet of Things (IoT) has driven numerous innovations in wireless communications that not only demand technical efficiency but also raise philosophical questions about the nature of scientific knowledge. One such innovation is Physical Layer Network Coding (PLNC), a communication technique that utilizes signal interference as a source of information to enhance system performance. This paper examines the philosophical dimensions of science within PLNC, focusing on three fundamental aspects: ontology, epistemology, and axiology. Ontologically, PLNC represents a new paradigm in wireless communication that reinterprets interference not merely as noise but as an opportunity. Epistemologically, knowledge of PLNC is derived through scientific methods such as mathematical modeling, experimentation, and simulation—yielding intersubjective and verifiable truths. Axiologically, PLNC holds practical value in terms of energy efficiency, data reliability, and contributions to the sustainability of IoT ecosystems, while also raising ethical considerations regarding privacy and information security. Thus, this study demonstrates that the development of PLNC cannot be separated from philosophical reflection, emphasizing the profound interconnection between technological advancement, scientific methodology, and human values.

This study aims to design and develop a functionally efficient and operationally effective Internet of Things (IoT)-based air quality monitoring system for radiology departments. The system utilises a DHT22 sensor integrated with an ESP32 microcontroller to monitor the temperature and humidity of diagnostic rooms in real time, and to display the data via the UdaraKu mobile application. The research method employed a quantitative experimental approach focused on measuring system performance, specifically the accuracy of the temperature and humidity sensors. The research model used was the Research and Development (R&D) method, aimed at transforming conventional air quality monitoring in radiology into a real-time digital system based on IoT. The research results indicate that the IoT-based monitoring system is capable of maintaining room temperature and humidity stability within the ideal range, namely 22–24°C and 50–60% RH, in accordance with international standards. This improvement in environmental stability has a direct impact on reducing noise in digital radiography images, as evidenced by an increase in the Signal-to-Noise Ratio (SNR). Instrument validation demonstrated a high level of reliability with a Cronbach’s Alpha value of 0.848, reinforcing the reliability of the data and the system. Overall, the IoT-based air quality monitoring system has proven effective in controlling noise in digital radiography images, improving the quality of diagnostic services, and supporting patient safety principles and operational efficiency within radiology departments.

The rapid development of technology in the era of the Industrial Revolution 4.0 has driven the education sector to continuously adapt to the evolving demands of digital-based industries. One of the key technological innovations supporting this transformation is the Internet of Things (IoT), which enables data acquisition, real-time monitoring, and remote control of systems through internet networks. In response to these developments, a community service program was conducted to enhance the understanding and technical skills of students at SMK Negeri 1 Sindang through the provision and utilization of an IoT Trainer Kit Simulator as a practical learning medium. This activity aimed to bridge the gap between theoretical knowledge and industry-relevant technological applications by introducing students to hands-on IoT system implementation. The program included demonstrations and guided practice on the use of sensors, microcontrollers, and web-based monitoring platforms to simulate real-world industrial scenarios. The results indicate that students showed high enthusiasm and active participation throughout the activity. Moreover, participants were able to grasp the fundamental concepts of IoT systems, understand component integration, and recognize the relevance of IoT applications in supporting automation and digital transformation. Overall, this community service activity contributed positively to strengthening students’ digital competencies and preparedness for the demands of the contemporary industrial and technological landscape.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

In the modern era, embedded systems and the Internet of Things (IoT) are experiencing rapid and continuous development, with their usage projected to match or even exceed the number of IoT devices globally. This technological transformation has influenced various sectors, including education, which must adapt to current digital advancements. Therefore, it is important for educational institutions to integrate embedded systems and IoT concepts into their curricula to ensure that learning remains relevant to industrial and technological demands. Improving the competence and technological literacy of students and lecturers in the fields of embedded systems and IoT, particularly for smart control applications, is expected to strengthen academic capabilities and practical skills. Through this development, higher education institutions can encourage innovation and the application of technology-based solutions in solving real-world problems. Furthermore, this effort supports the implementation of the Tri Dharma of Higher Education, especially in the areas of education and research, by promoting applied research projects and collaborative learning activities based on embedded systems and IoT technologies.

In the modern era, embedded systems and the Internet of Things (IoT) are experiencing rapid and continuous development, with their usage projected to match or even exceed the number of IoT devices globally. This technological transformation has influenced various sectors, including education, which must adapt to current digital advancements. Therefore, it is important for educational institutions to integrate embedded systems and IoT concepts into their curricula to ensure that learning remains relevant to industrial and technological demands. Improving the competence and technological literacy of students and lecturers in the fields of embedded systems and IoT, particularly for smart control applications, is expected to strengthen academic capabilities and practical skills. Through this development, higher education institutions can encourage innovation and the application of technology-based solutions in solving real-world problems. Furthermore, this effort supports the implementation of the Tri Dharma of Higher Education, especially in the areas of education and research, by promoting applied research projects and collaborative learning activities based on embedded systems and IoT technologies.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.