Publication Search

This research develops an automated temperature control system for water heaters, that is both efficient and stable, driven by the need for energy-saving heating solutions, particularly for marine applications. The main objective was to create an automatic system capable of maintaining water temperature within an optimal range while maximizing energy efficiency through the use of thermal storage materials. The methodology involved an on-off control system based on a microcontroller as the main controller, capable of processing temperature sensor data in real time. This system intelligently activates and deactivates the heater to keep the water temperature stable. Paraffin wax was used as a latent heat storage medium, playing a crucial role in gradually storing and releasing thermal energy to support temperature stability. An automatic water heater system based on the ESP32 microcontroller with an on-off control mechanism was successfully designed using paraffin wax combined with silica sand as a thermal storage medium. The addition of silica sand significantly enhanced heat conductivity and temperature stability. Test results showed a substantial reduction in energy consumption, with daily savings reaching Rp12,762.88, equivalent to 67% of total daily energy costs. Over a one-year period, the total savings amounted to Rp4,658,103.20, demonstrating that the paraffin wax–silica sand combination is highly effective in improving energy efficiency and reducing long-term operational costs.

The need for environmentally friendly marine transportation continues to increase, especially in tourist areas such as Penyengat Island, Tanjungpinang, Riau Islands. In the local context, the design of electric boats has not been widely developed for small islands in Indonesia, even though the potential and urgency for its application is very high. Geographical constraints, limited charging infrastructure, and a lack of technical data are the main obstacles. This research focuses on the design and construction of an electric-powered tourist boat prototype with an asymmetrical catamaran hull type as an effort to support a sustainable transportation system. The boat design applies an asymmetrical catamaran hull configuration to improve sailing stability and energy efficiency. The propulsion system is controlled via a wireless PS2 joystick integrated with an ESP32 microcontroller, supported by a 24V DC motor powered by lithium-ion batteries and solar panels as a supplement. Test results show that the boat can operate stably with remote control, good energy efficiency, and zero emissions during operation. Some technical challenges encountered include limited operational duration due to battery capacity and high sensitivity of electronic components to water exposure. Overall, this prototype has great potential for further development as an environmentally friendly alternative for maritime transportation system. The stable catamaran hull design provides advantages in terms of comfort and safety, especially in calm or shallow waters. The electric propulsion system used has proven to be efficient and responsive, supported by a remote control mechanism that is easy to operate via a wireless joystick

This community service program aims to strengthen the competence of automotive workshop MSMEs in mastering microcontroller-based starter system technology, enabling them to adapt to the era of vehicle digitalization. The program was carried out through several stages: identification of training needs, delivery of basic theory on microcontrollers and starter systems, practical sessions in module assembly and programming, followed by mentoring and evaluation. The results of the evaluation showed an 85% increase in participants’ understanding of the digital starter system concept, an 80% improvement in module assembly and programming skills, and a 75% increase in readiness to apply innovative starter systems in workshop services. The novelty of this activity lies in the development of a more applicable digital starter system model tailored to the needs of small-scale workshops. The implementation emphasized a participatory, practice-based approach using learning by doing. Participants, mainly workshop owners and technicians, were guided step by step from component recognition, software operation with Arduino IDE, to the prototyping of functional microcontroller-based starter systems. To ensure sustainability, the program also provided training modules, video tutorials, and online mentoring, allowing participants to continue practicing independently after the training. Beyond improving technical competence, this activity also fostered an innovative mindset among participants, making them more confident to provide modern, technology-oriented services. In the long term, the program is expected to enhance the competitiveness of automotive MSMEs, strengthen customer trust, and build collaborative networks between higher education institutions and local workshops in supporting vocational education and digital transformation.

Liquefied Petroleum Gas (LPG) leakage remains one of the leading causes of fires in both household and industrial environments, highlighting the urgent need for early detection systems that provide fast and accurate warnings. This study aims to design and implement an LPG leakage detection device based on the ESP8266 microcontroller, utilizing the MQ-6 gas sensor and a real-time notification system through Telegram Bot integration. The system operates by reading LPG gas concentration via the MQ-6 sensor, which is then processed by the ESP8266 to automatically activate a buzzer alarm and a DC fan when gas levels exceed the threshold of 200 ppm. Additionally, the system sends instant alert notifications to the user through the Telegram application, allowing remote monitoring of gas conditions. Real-time gas concentration data are also displayed using a 16x2 LCD for direct visualization. System testing was carried out through controlled gas leakage simulations, and the results showed that the device is capable of responding rapidly, with a reaction time of less than 2 seconds after detection, while consistently providing accurate warnings. The integration of Internet of Things (IoT) technology significantly enhances the effectiveness and efficiency of the system, enabling practical and accessible monitoring. Overall, the developed device demonstrates strong potential as an innovative solution to improve safety, minimize fire risks, and raise public awareness regarding the hazards of LPG leakage, making it suitable for practical application in households and small industries.

As an archipelagic country, Indonesia has significant potential for the utilization of renewable energy, particularly wind energy in maritime areas with low wind speeds (3–6 m/s). This study aims to design and test a vertical Savonius wind turbine system equipped with a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm as a power source for shipboard water heating systems. The research method applied is Research and Development (R&D), integrating several components such as a DC generator, full-wave bridge rectifier, INA219 current sensor, anemometer cup sensor, ESP32 microcontroller, and a monitoring interface utilizing Google Spreadsheet and a 20x4 LCD. The system was tested under two operating conditions: without MPPT and with MPPT. The experimental results show that the application of the MPPT algorithm successfully increased power output by up to 272.64% while maintaining voltage stability despite varying wind speeds. Nevertheless, the average output power of 2.605 W remained insufficient to meet water heating requirements within a short time. For example, charging a 12V 50Ah battery would require approximately 9.6 consecutive days of operation, highlighting the system’s limitations in high-demand scenarios. Despite these constraints, the findings demonstrate that the vertical Savonius wind turbine integrated with MPPT has strong potential as a clean and environmentally friendly alternative energy solution for maritime applications, particularly for small-scale onboard electrical loads. This study contributes to renewable energy utilization in the shipping sector and provides a foundation for further technological development and optimization.

Water heating systems on ships play a crucial role in supporting crew comfort and operations. However, conventional systems generally operate continuously without automatic control, resulting in energy waste and high operational costs. This situation drives the need for innovation in more efficient and environmentally friendly water heater designs. This research aims to design and implement an automated water heater temperature control system based on an ESP32 microcontroller with on/off control and the use of paraffin wax as a latent heat storage medium. This design is expected to improve energy efficiency while reducing the operational costs of water heaters on ships. The research methodology includes system design, hardware assembly, and performance testing, both static and dynamic. Testing was conducted to evaluate temperature stability and energy efficiency in system conditions with and without the use of paraffin wax. The results showed that paraffin wax improved water temperature stability while reducing the frequency of heater activation. In a 24-hour test, the system without paraffin wax recorded a heater operating time of 15 hours, consuming 8.85 kWh of electricity and costing Rp11,965.20. In contrast, the Paraffin Wax system only requires 10 hours of heater operation, consumes 5.90 kWh, and costs Rp7,976.80. This demonstrates energy savings of 2.95 kWh and a cost efficiency of up to 58%. Therefore, the ESP32-based water heater automation system and heat storage using Paraffin Wax are proven to be more energy-efficient, cost-effective, and have the potential to be an innovative and environmentally friendly solution for ship operational needs.

Examinations in radiology installations have increased annually, resulting in a higher risk of radiation hazards to staff, particularly in interventional, conventional X-ray, and CT-Scan environments. Furthermore, not all hospitals are equipped with radiation survey meter monitoring devices. Therefore, an innovative radiation exposure monitoring system based on the Internet of Things (IoT) was developed. This study aims to: (1) develop an IoT-based radiation exposure monitoring system, (2) evaluate the device’s performance, and (3) assess the effectiveness of the system. The research method used was research and development (R&D), which included preliminary information gathering, product planning and development, expert validation, device performance testing, effectiveness testing, and final outcomes. This system was developed for real-time radiation dose monitoring.  The product development involved a Geiger-Muller M4011, an ESP32 microcontroller, and a Node-Red web application. Performance testing of the IoT-based radiation exposure monitoring device and a standard survey meter in CT-Scan and conventional X-ray rooms used the T-Test and Mann-Whitney tests. The results showed p-values > 0.05, indicating no significant difference between the two devices. Effectiveness testing through pre-test and post-test questionnaires demonstrated improved usability—namely flexibility, operability, learnability, and understandability—with an N-Gain value of 78.95%, indicating effectiveness. In conclusion, the IoT-based radiation exposure monitoring system proved effective and feasible for real-time radiation dose monitoring in radiology installations, offering advantages in IoT-based data integration, ease of access, and structured data recording. In addition to performance testing, the system was evaluated in terms of its integration with hospital infrastructure. It was designed to provide real-time radiation dose monitoring, automatically updating the dose data and sending alerts when safe exposure levels are exceeded. The system also enables remote monitoring, making it easier for radiology departments to oversee radiation safety without the need for direct physical checks.

This research was conducted from June to July 2025 in Binjai City, with the primary focus being analyzing the readiness of the Binjai City Regional Disaster Management Agency (BPBD) to implement a flood early warning system utilizing artificial intelligence (AI). The data collection process was conducted through a literature review, which involved reviewing various theories and previous research results regarding the application of AI and Internet of Things (IoT) technology in the context of disaster mitigation. Based on the results of the study, it was found that the use of technologies such as ultrasonic sensors, microcontrollers, fuzzy logic, and automatic notification systems can provide real-time warnings with a high level of accuracy and a fast response. This system enables early detection of rising river levels through automatic measurements, intelligent data processing, and sending notifications to authorities and affected communities within seconds. By integrating historical data and machine learning-based predictions, this system is also able to depict potential flooding before it occurs, providing a longer response time for evacuation. However, the readiness of the Binjai City BPBD still faces various challenges, such as limited digital infrastructure, the need for human resource training in the technology field, and inadequate budget allocation. Therefore, cross-sector collaboration and ongoing policy support are needed for optimal implementation of this system. The use of AI and IoT in early warning systems is not only technically relevant but also urgent in the face of increasing climate change and flood risks. A strategy involving cross-sector collaboration between government, academia, and the private sector is needed to develop an adaptive and sustainable early warning system.

Internet of Things (IoT)-based digital transformation has become a major catalyst in improving the efficiency of operational systems in various sectors, including the modern retail industry. One of the common logistics problems found in supermarket environments is the accumulation of unorganized shopping trolleys, which can hinder service flow and increase staff workload. This study presents a design of an IoT-based autonomous smart trolley system and automatic navigation to address these problems in a structured manner. The system design utilizes the integration of ESP32 and Arduino UNO microcontrollers, ultrasonic sensors for distance detection, line sensors for automatic path navigation, and Raspberry Pi modules for visual image processing in location tracking. The system is designed to be able to independently reposition the trolley to a predetermined parking station. Conceptual analysis shows that this system has significant potential in reducing operational costs, increasing labor efficiency, and strengthening customer service automation. Initial evaluation of technical and economic feasibility aspects strengthens the opportunity for widespread system implementation in the future. This design is the first step in developing a smart retail solution based on adaptive technology that is in line with the principles of Society 5.0. Furthermore, the development of this smart trolley system also considers user safety and comfort through additional features such as anti-collision sensors, an early warning system in the event of technical problems, and a manual control option as an alternative in emergency situations. The integration of Internet of Things-based technology also enables real-time monitoring and management systems through a web-based dashboard or mobile application, which can be accessed by supermarket management for operational analysis. Thus, this system not only addresses internal logistics needs but also contributes to improving the overall customer experience.

The rapid advancement of information technology has brought significant transformation across multiple sectors, including library management systems. One of the pressing needs in higher education libraries is the development of an efficient, accurate, and secure borrowing system that reduces human error and improves service quality. This study aims to design and implement a book borrowing system for the STMIK Kaputama library by utilizing Radio Frequency Identification (RFID) technology integrated with the Internet of Things (IoT). The proposed system employs the ESP32 microcontroller as the core controller, supported by the RFID RC522 module and buzzer as primary devices to facilitate the automatic identification of library members and borrowed books. The borrowing and returning process is executed through RFID card scanning, with all transaction data transmitted via WiFi and stored in a MySQL database, which is managed through a PHP-based web platform. The development process follows the Agile methodology, enabling iterative improvement and adaptability to user requirements. System testing demonstrates that the integration of RFID and IoT technology significantly enhances the efficiency, accuracy, and speed of library operations compared to the previous manual system. Furthermore, the system allows real-time recording of borrowing transactions, monitoring of user activities, and centralized data management. These features not only streamline library services but also improve data security and reduce the risk of loss or duplication of records. Overall, the implementation of this RFID-IoT-based system provides an innovative solution for modernizing library management, particularly in higher education institutions. The system ensures faster services, minimizes errors, and creates a structured and reliable digital infrastructure to support academic information services. This study highlights the potential of combining RFID and IoT technologies to improve the quality and effectiveness of library systems in the digital era.

Maintaining stable cage temperature is a crucial factor in the success of broiler chicken farming, especially in close house systems that rely on optimal microclimate control. Temperature instability can lead to thermal stress, reduced growth rates, and increased mortality in broilers, particularly during the early stages of life (0 to 30 days old). This study aims to design and implement an automatic temperature control system based on the Arduino Uno microcontroller integrated with a DHT11 temperature and humidity sensor in the broiler chicken cages of PT. Barokah Restu Utama. The system is designed to read temperature and humidity in real-time and automatically activate or deactivate cooling devices such as fans or heating devices like incandescent lamps, depending on the temperature range required for each growth phase. The ideal temperature range used as a reference in this system includes 30–32°C for chickens aged 0–7 days, 29°C for ages 8–14 days, 28°C for ages 15–21 days, and 26–27°C for chickens aged 22–30 days. Testing results show that the system is capable of maintaining stable temperatures according to the specified standards for each growth phase. With this automatic control system in place, broiler chicken maintenance becomes more efficient and effective. The risk of mortality due to heat stress is significantly reduced, and chicken growth becomes more optimal. This technology offers a practical and economical solution, especially for small- to medium-scale broiler chicken farmers who use close house systems. The system is easy to operate and relatively affordable to install, making it an accessible innovation that supports better livestock management through automation and smart farming practices.

This study focuses on the development of an interactive web-based learning platform for Proportional-Integral-Derivative (PID) control systems, aimed at addressing the conceptual challenges faced by electrical engineering students when learning PID through conventional teaching methods. Despite its foundational role in control theory, PID remains difficult to grasp without practical visualization and hands-on experimentation. To bridge this gap, the research introduces a practical and accessible platform that enhances conceptual understanding through real-time simulations and physical interaction. The proposed system integrates key hardware components including an ESP-32 microcontroller, DC motor, rotary encoder, BTS 7960 motor driver, and I2C LCD. The platform’s web interface is built using HTML, Tailwind CSS, and JavaScript, enabling intuitive user interaction. Motor response data is captured via the ESP-32 and transmitted to the web interface using the WebSocket protocol, allowing users to instantly visualize system behavior as PID parameters (Kp, Ki, Kd) are adjusted. This dynamic feedback mechanism enables students to observe changes in system characteristics such as rise time, overshoot, and settling time in real time. To evaluate the platform’s feasibility, practicality, and educational effectiveness, beta testing was conducted among electrical engineering students using Likert-scale questionnaires. The results demonstrated that users were able to successfully interpret the impact of PID tuning on system performance. The average evaluation score reached 75.13%, indicating strong agreement regarding the platform’s educational value and its effectiveness in enhancing learning outcomes. In conclusion, the study affirms that the developed web-based platform offers a feasible, engaging, and pedagogically effective alternative to traditional learning approaches. By combining interactive simulations with physical experimentation, the platform significantly improves students’ understanding of PID control systems.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring. 

 The Karanggeneng River in Rembang Regency, Central Java, serves as the main water source for the surrounding community but is vulnerable to seawater contamination during the dry season due to decreasing river elevation. To address this issue, this study aims to design and implement a river water elevation monitoring device based on the Internet of Things (IoT) powered by a hybrid Solar Power Plant (PLTS). The device utilizes the MB7360 ultrasonic sensor connected to an ESP32 microcontroller to measure water elevation in real-time and display the data through an LCD and the Blynk application on a smartphone. The methodology includes literature review, device design, system implementation, and field performance testing. Test results show that the sensor can measure water height accurately within a range of 30 cm to 5 meters, and the PLTS system is capable of supplying the required 0.56 Watts of power. The study compared two alternative solutions and selected the ESP32-based system as the best option due to its efficiency, cost-effectiveness, and easy-to-source components. The conclusion of this research indicates that the developed device can provide accurate and continuous information, support monitoring of river conditions to prevent the risk of seawater intrusion, flooding, or drought, and has the potential to be applied as a mobile system in various other river locations across Indonesia.

In the modern era, the need for sophisticated vehicle security systems is increasing along with the high rate of motor vehicle theft. This research designs and implements a fingerprint sensor-based vehicle security system using an Arduino Uno microcontroller. This system aims to improve security by utilizing biometric technology that can only be accessed by verified users. The fingerprint sensor is used to recognize the user's fingerprint, then activate the system through a relay instead of a conventional key. System testing shows a fast response time with an estimated rise time of about 0.5 seconds and settling time of about 2 seconds, without any misidentification (false positive or false negative). Thus, the system is proven to provide higher security, good authentication speed, and ease of use compared to conventional security systems. These results show that the implementation of biometric technology in vehicles has the potential to be widely applied.

Engine overheating is a critical condition that can cause damage to internal components, reduce operational efficiency, and lead to overall system failure. The absence of an automatic protection system is one of the primary factors contributing to damage. This study aims to design and test a temperature sensor-based safety device that can automatically cut off the engine's working system when the temperature exceeds the safe limit. The method used is descriptive statistical analysis to evaluate the effect of independent variables (operational time duration and workload) on the dependent variable (coolant temperature). The system was tested through water heating simulation using an electric heater and controlled by an Arduino Nano microcontroller, a MAX6675 temperature sensor. Testing was carried out with variations in power load (150, 300, 450, 600, and 750 Watts) and operational duration (20, 40, and 60 minutes). Temperature data were collected and analyzed using ANOVA to determine the effect of load and time on temperature increase. The results showed that the temperature increased significantly with increasing power load, with temperatures approaching 100°C at loads ≥450 Watts in less than 20 minutes. The load variable shows a significant effect on temperature (p < 0.05), while the duration of time shows a nonlinear but not statistically significant upward trend. The safety device is proven to be able to automatically disconnect the system when the temperature reaches the specified maximum limit, thus effectively preventing engine damage due to overheating.

This study aims to develop an automatic detection system to improve lift safety through early detection and rapid response to sling failure. The research method uses an experimental approach by designing a 3-story lift prototype that integrates a Raspberry Pi Pico microcontroller as the main processing unit, an HC-SR04 ultrasonic sensor to detect lift position, a limit switch to detect sling failure, and a mechanical braking system using a servo motor. The system is equipped with an I2C LCD for real-time display, Telegram notifications for remote monitoring, and a buzzer alarm as an early warning. The test results show that the ultrasonic sensor has a high accuracy of 97.58% with an average error of 2.42%. The system successfully detects sling failure and activates mechanical braking automatically, preventing the basket from falling freely and allowing it to stop on the nearest floor. All control functions such as navigation buttons, motor rotation direction, and limit switch accuracy work well. This system provides an innovative solution to improve elevator safety through the integration of automatic detection technology, real-time monitoring, and direct physical response that can be applied to the development of future elevator safety technology.

Sterilization occupies a fundamental position in the domain of dentistry, ensuring that the instruments utilized are entirely devoid of pathogenic microorganisms. An autoclave sterilization apparatus employing the dry heat technique, based on the Arduino Uno platform, is essential in providing a robust and effective methodology for the sterilization of dental instruments. The hardware configuration is comprised of a heating element, a temperature sensor, and a control system that is meticulously designed to uphold temperature with precision and reliability. The sterilization of dental instruments is imperative for upholding hygiene standards and averting cross-contamination. The primary aim of this research is to devise a sterilization device that is both efficient and effective through the implementation of the dry heat method, which is regarded as safer in comparison to traditional steam-based techniques. The architecture of this autoclave incorporates an Arduino Uno microcontroller as the central controller of the system, which autonomously regulates the temperature and duration of the sterilization cycle. The temperature sensor and heating element are systematically interconnected to maintain the temperature within the optimal range (100-150°C), which is critical for effective dry heat sterilization. Furthermore, a user-friendly interface has been integrated to facilitate the oversight of the sterilization process. This apparatus is adept at executing the sterilization procedure efficiently within a reduced timeframe, without compromising the integrity of the sterilization results. The system provides enhanced control and can be tailored to meet specific user requirements, thereby positioning it as an ideal solution for cost-sensitive dental clinics. This innovation is expected to significantly elevate hygiene standards within dental practices and may stimulate considerable advancements in the healthcare sector.

Electric Vehicles Require A Reliable Monitoring System To Maintain Their Efficiency And Safety. This Study Designs A Monitoring System For Battery Condition And Vehicle Location Based On The Esp32 Microcontroller And The Mit App Inventor Application. The System Uses The Pzem 017 Sensor To Monitor Voltage, Current, Power, Energy, As Well As Battery Capacity And Health, And The Neo-7m Gps Module For Location Tracking. Data Is Displayed In Real Time Through An Android Application. Test Results Show That The System Provides Accurate Information, With Minimal And Statistically Insignificant Measurement Errors According To The Paired T-Test. The System Also Remains Stable At Various Vehicle Speeds (Anova), And The Location Tracking Demonstrates An Average Accuracy Of 4.77 Meters In Open Areas. In Enclosed Areas, The Error Increases To An Average Of 10.26 Meters. The System Is Also Capable Of Automatically Calculating Battery Capacity And Health. Overall, The Developed System Proves To Be Effective, Efficient, And Flexible In Supporting The Reliability And Safety Of Electric Vehicles.

This study aims to design and develop a pipeline leakage detection system based on the Internet of Things (IoT) that provides real-time notifications and determines the location of leaks with high accuracy. Pipeline leakage is a serious issue, as it can lead to water wastage, environmental damage, and high maintenance costs. Therefore, a system that can detect leaks quickly and accurately is crucial for improving the efficiency of pipeline infrastructure management. The system developed in this study uses an ESP32 microcontroller, Waterflow sensor, and GPS module. The ESP32 microcontroller serves as the central processing unit that processes the data received from the Waterflow sensor and the GPS module. The Waterflow sensor detects changes in water flow that indicate a leak in the pipeline. When an abnormal reduction in flow is detected, the sensor sends a signal to the microcontroller. The GPS module then provides location coordinates, pinpointing the exact location of the leak, allowing the maintenance team to quickly address the issue. Additionally, the system is integrated with the Blynk application, which enables remote monitoring through a mobile device. The Blynk application provides a user interface that facilitates the monitoring of pipeline status and delivers notifications whenever a leak is detected. Testing results show that the IoT-based leakage detection system is capable of identifying leaks and sending real-time information with good accuracy. With this system, the process of identifying and addressing pipeline leaks can be done faster and more efficiently, ultimately reducing the losses caused by leakage. The system also offers a more effective solution for pipeline maintenance, improving the reliability of water distribution systems and reducing water resource wastage.