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This study reports the green synthesis of vanadium pentoxide (V₂O₅) using virgin olive oil as a natural and environmentally benign reducing agent. The approach aims to minimize the environmental impacts associated with conventional synthesis routes. Structural and physicochemical characterizations confirmed the successful formation of nanoscale V₂O₅. X‑ray diffraction (XRD) analysis indicated an average crystallite size of approximately 16.57 nm, evidencing high crystallinity. Fourier‑transform infrared spectroscopy (FTIR) revealed characteristic V=O and V–O–V vibrations with bands associated with physisorbed water, confirming the correct oxide framework. Field‑emission scanning electron microscopy (FE‑SEM) showed irregularly shaped nanoparticles with a representative particle diameter of ~32.62 nm. Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) analyses yielded a specific surface area of 10.817 m²/g, a total pore volume of 0.024277 cm³/g, and a broad mesoporous distribution (20–90 nm). Energy‑dispersive X‑ray spectroscopy (EDX) confirmed the purity of V₂O₅ with weight fractions of V (69.40%) and O (30.60%), consistent with the stoichiometric composition. Overall, the results demonstrate the effectiveness of olive oil as a green reducing agent for preparing nanoscale V₂O₅, which is promising for catalysis, energy storage, sensors, and clean‑energy applications.

The braking system is a crucial component in a vehicle, where its performance is highly influenced by the wheel's rotational speed and the geometry of the brake pad. This study aims to analyze the impact of wheel rotational speed and variations in pad geometry on the temperature of the brake pad. The braking process generates heat due to friction, which, if not properly managed, can reduce braking performance and accelerate brake pad wear. The experiment was conducted at four levels of wheel rotational speed: 1000 RPM, 1500 RPM, 2000 RPM, and 2500 RPM. The testing system was designed using a braking system simulator equipped with a speed sensor (LM393) and a temperature sensor (K-type thermocouple), which were connected to an Arduino microcontroller and displayed in real-time through a Graphical User Interface (GUI) in MATLAB. The test results indicated that both the geometric shape of the brake pads and the wheel rotation speed significantly affected the resulting temperature. Standard brake pads produced the highest temperature at a speed of 2500 RPM, reaching 63.33°C. In contrast, brake pads with holes offered the best performance by maintaining a lower temperature of only 43.00°C. Furthermore, an increase in wheel rotation speed led to a noticeable rise in temperature; for standard pads, the temperature increased from 36.67°C at 1000 RPM to 63.33°C at 2500 RPM. This demonstrates that RPM is a major factor in heat generation due to friction. The MATLAB GUI effectively visualized the relationship between RPM and temperature, facilitating the analysis and evaluation of the data.

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.

Waste management remains a major challenge, particularly due to the lack of public awareness and habits in sorting waste from its source. Innovative technology-based solutions are needed to support more effective waste management systems. This study aims to design and develop an Internet of Things (IoT)-based smart trash bin capable of automatically sorting metal and non-metal waste. The system utilizes an ESP32 microcontroller as the main controller, an LJ12A3-4-Z/BY sensor to detect metal materials, an E18 sensor to detect non-metal materials, and an ultrasonic sensor to monitor bin capacity. The waste separation mechanism is operated by a servo motor controlled by the system, while the Blynk application is integrated for real-time monitoring and full-capacity notifications sent directly to the user’s smartphone. The research stages included hardware design, microcontroller programming, IoT platform integration, and functionality testing to ensure system performance and reliability. The results showed that the smart trash bin operated as expected, successfully identifying and separating metal and non-metal waste automatically, while also sending real-time notifications to the user when the bin approached full capacity. The implementation of this technology has the potential to modernize waste management processes, improve the efficiency of waste sorting, and reduce dependency on manual labor. Furthermore, this research opens opportunities for future development by integrating additional sensors and cloud-based data management systems to support smart city initiatives and sustainable waste management practices.

Android continues to innovate, and biometric systems particularly fingerprint sensors must operate with high efficiency. However, the main challenge lies in the communication between hardware and software, which often causes high latency, excessive power consumption, and protocol incompatibility between components. As a result, authentication speed decreases and system stability is compromised, especially on mid- to low-range devices. This study explores various implementations of Android-based fingerprint systems, focusing on how sensor modules interact with the microcontroller or Trusted Execution Environment (TEE) and the operating system through a hardware-software co-design approach to evaluate integration efficiency across all layers. The analysis reveals that conventional protocols such as Bluetooth or serial connections still cause delays, while improvements in drivers and the Hardware Abstraction Layer (HAL) can significantly reduce latency. As a solution, the researchers propose a co-design optimization approach that utilizes data flow normalization within the HAL and adopts lightweight communication protocols to accelerate the verification process. Based on the test results, this approach successfully improves efficiency—authentication time is reduced by up to 35% and power consumption decreases by approximately 15%. Therefore, the efficiency of communication between hardware and software becomes a key factor in enhancing the performance and reliability of fingerprint systems on Android devices.

Unmonitored traffic conditions often hinder decision-making processes in traffic management, particularly on secondary roads. Jalan Raden Inten II in East Jakarta is one of the connecting routes with heavy traffic activity at certain times, yet no integrated data-based monitoring system is currently available. This study proposes an Internet of Things (IoT)-based traffic condition classification system to identify Clear, Normal, or Congested states based on vehicle counts and speed categorization. The system is designed using an ESP32 microcontroller, an HB100 sensor to detect vehicle speed, and two AJ-SR04M ultrasonic sensors to detect vehicle presence. Data on vehicle counts and the percentage of slow-moving vehicles are periodically transmitted to the ThingSpeak platform and processed using the Threshold-Based Classification method. The classification results are visualized on a dashboard-based website equipped with charts, traffic condition status, and notifications when consecutive congestion is detected. Testing was conducted using simulation data over a specific period. Qualitative validation was carried out by comparing the classification results with traffic indicators from Google Maps. The results show that the system can classify traffic conditions with a good degree of agreement with external references, although discrepancies occurred at certain times due to the limitations of simulated data. This research demonstrates that a simple IoT approach can provide an affordable and effective solution for monitoring and classifying traffic conditions, with potential for real-world implementation in future studies.

Conventional attendance systems often face various problems such as inefficiency, inaccuracies in attendance logging, and limitations in recapitulation processes. Manual systems are prone to human error and time-consuming, while fingerprint-based systems may fail when the sensor is affected by dirty, wet, or damaged fingers. This study aims to develop an attendance system based on Artificial Intelligence (AI) by utilizing the face_recognition function in Python and implementing a microservice architecture to improve efficiency and accuracy in attendance recording. The system is developed using the Agile Feature-Driven Development (FDD) method, which focuses on building system features based on prioritized business values. This method is applied within the Software Development Life Cycle (SDLC) to ensure a structured, iterative, and user-oriented development process. Facial recognition is performed by comparing the encoding of the captured face image with the data stored in the database. The results show that the system is capable of recording attendance automatically, accurately, and in real-time. Furthermore, the recapitulation process becomes more efficient as the system manages and presents the data systematically.

The braking system was a crucial component in ensuring the safety of motor vehicles on the road. Brake failures, such as brake fade or total malfunction, could result in fatal accidents. Therefore, an accurate and realistic testing method was necessary to measure the effectiveness of a vehicle’s braking system. This study aimed to develop a deceleration measuring device based on GPS, which could be used in road test methods. The device was designed using an ESP32 microcontroller combined with a BN-220 GPS module, RTC real-time clock sensor, 16x2 LCD, thermal printer, and microSD storage media. Testing was conducted on a Mitsubishi L300 vehicle at the Depok Beach runway in Yogyakarta, with initial speed variations ranging from 10 to 80 km/h. Calibration results showed that the device had an accuracy of 99.64% with an average error of 0.36%. Meanwhile, the test results recorded deceleration values ranging from 0.74 to 3.26 m/s², depending on the initial speed and braking conditions. Based on these findings, the developed device proved to be accurate and feasible as an alternative method for field testing of braking systems.

This research presents the design and testing of an automatic color detection system using TCS3200 color sensor integrated with Arduino Uno microcontroller. The system was developed and tested using Wokwi virtual simulation platform before physical implementation. The TCS3200 sensor converts RGB light intensity reflected from objects into frequency signals, which are processed by Arduino Uno to classify colors into red, green, and blue categories. The system incorporates audio feedback using DFPlayer Mini module to provide sound notifications for detected colors. Testing results show that the system can accurately detect and classify primary colors with frequency-based thresholds: red (R<48 &R>37 & G<95 & G>85), blue (G<75 & G>65 & B<33 & B>23), and green (R<55 & R>40 & B<25 & B>5). The simulation validation demonstrates stable performance with consistent color recognition capabilities, making it suitable for industrial sorting applications and assistive technology for visually impaired individuals.

Blood transfusion is a critical medical procedure that requires the blood to be at a temperature close to normal human body temperature, approximately 36– 38°C. Transfusing cold blood can lead to serious complications such as hypothermia, coagulation disorders, and even cardiac arrest. Therefore, a reliable and automated blood warming device is essential to ensure safe transfusions. This study aims to design and modify a Blood Warmer Thawing device based on the Arduino Uno microcontroller as an innovative and cost- effective solution, particularly for healthcare facilities with limited resources. The system integrates a DS18B20 temperature sensor to monitor the temperature of the heating medium in real time, with data displayed on a 20x4 I2C LCD. Users can set the desired temperature via a 4x4 keypad, and the system automatically adjusts the heater performance based on the detected temperature. To enhance safety and operational efficiency, the device is also equipped with a buzzer as a warning indicator when the temperature exceeds the safe threshold, and utilizes a DC motor and cooling fan to maintain proper heat circulation. Test results indicate that the device is capable of maintaining the liquid temperature within the ideal range for blood transfusions, with high stability and fast response to temperature changes. This innovation offers a practical, affordable, and easy-to-implement solution to support effective and safe blood transfusion procedures in various healthcare settings.

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

The IoT-based CO and COHb gas detector is a device used to detect the presence of carbon monoxide (CO) gas and the level of carboxyhemoglobin (COHb), supported by Internet of Things (IoT) technology for remote monitoring and real-time reporting. This device will be used in hospital rooms, serving as an important solution in efforts to ensure the safety of patients, medical staff, and hospital visitors. With the presence of the CO and COHb gas detector, hospital staff will find it easier to detect the presence of CO gas in the rooms within the hospital. The operation of this device is quite simple: just place the device in the room in the "on" condition, and it will automatically detect the CO and COHb gas present in that room. The measurement results of CO and COHb gas will be directly sent to Google Sheets for storage of the measurement results. The creation of this device requires several components, such as the ESP32 as the microcontroller, DHT11 as the temperature detection sensor, MQ7 as the CO gas detection sensor, and the application of IoT using a Google Sheets web app to monitor results remotely and store measurement results. According to the analysis of the device created by the researchers, the performance or functionality test results of the CO and COHb gas detector can be used smoothly. The author conducted functional tests or performance tests of the device by measuring data in a free room, roadside environment, motorcycle exhaust smoke, active smokers, and passive smokers.

Technological advancements, particularly in microcontrollers and the Internet of Things (IoT), have brought significant changes to various aspects of life. Microcontrollers such as the ESP32, DS18B20 sensor, water pH sensor, and other supporting sensors enable the development of efficient automation systems. This research addresses issues related to the design and development of IoT devices for water quality monitoring, as well as the integration of these devices with web-based systems. The objective is to develop a device capable of monitoring water quality parameters such as pH, temperature, turbidity, dissolved materials, salinity projection, and dissolved oxygen projection, while providing automatic notifications under certain conditions. The development process applies the Agile methodology, which allows for rapid and adaptive iterations. Theoretically, this study contributes to improving understanding of IoT implementation and information systems, while practically, it provides a technological solution to support Koi fish farmers in monitoring and managing pond water quality. The expected outcome is a web-based water quality monitoring and management device for Koi ponds that can deliver real-time information and condition notifications, thereby reducing aquaculture risks and improving productivity.

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

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

This community service initiative was carried out with the aim of introducing and training students of SMP Islam Sawahan Turen in programming and utilizing Arduino and ESP32 microcontrollers as a foundation for understanding modern technology. The workshop was specifically designed to provide students with knowledge of basic electronic concepts, sensors, and data communication, while also equipping them with practical skills to create simple microcontroller-based projects. During the sessions, participants received explanations about Arduino and ESP32, including how to connect sensors, process collected data, and transmit information to the Internet of Things (IoT) platform. In addition to the theoretical materials delivered, students were given opportunities to engage in direct practice under the guidance of experienced instructors, allowing them to gain a deeper comprehension of how the devices function. The combination of theory and practice helped students strengthen both their conceptual understanding and their technical abilities. Furthermore, the activity aimed to foster creativity and curiosity among participants in designing innovative projects that have real-life applications. This spirit of exploration is expected to nurture problem-solving skills, adaptability, and technological awareness from an early age. Beyond simply broadening students’ technological insights, the program also contributes to the enhancement of their competencies and preparedness in facing challenges of the digital era, ensuring that they can continue to grow as innovative learners and future innovators.

Traditional herbal medicine MSMEs face challenges in increasing the appeal of their products among the younger generation who prefer fresh and modern drinks. To overcome this, innovation in the presentation of herbal medicine is needed to make it more practical, hygienic and attractive. One solution is the manufacture of a microcontroller-based portable box cooler that can maintain the freshness of herbal medicine at an optimal temperature. This box cooler is equipped with an automatic cooling system controlled by a microcontroller, ensuring the jamu stays fresh and high quality for a longer period of time. A temperature sensor monitors and adjusts the cooling conditions as needed, so that the herbs remain at their best when served. In addition, the automatic dispenser system helps regulate the volume of herbs dispensed precisely, reducing wastage and maintaining flavour consistency. With its portable and efficient design, this box cooler is perfect for use in various locations such as bazaars, cafes, offices, or community events. This innovation not only improves the efficiency and competitiveness of MSMEs, but also reinforces the image of jamu as a modern and contemporary healthy drink, making it more attractive to young consumers.

This research aims to develop and evaluate the performance of a steam plant prototype designed as an alternative source of electrical energy to support the Vessel to Grid (V2G) concept. Utilization of backup energy on ships is becoming important as electricity demand increases and demands for a more sustainable electrical system. This system relies on ESP32 microcontroller technology as a control center that functions to monitor and control several key parameters, including steam pressure, combustion temperature, boiler water level, and the generated electrical voltage. The research method used is an experiment with a static and dynamic testing approach. Static testing is carried out to measure the performance of main components such as the boiler, turbine, and generator separately, while dynamic testing focuses on evaluating the overall system by involving the integration of sensors and supporting actuators. The test data is then analyzed quantitatively to determine the system's response to variations in steam pressure, temperature, and other operational conditions. The results show that the steam produced by the boiler is able to rotate the turbine, thereby driving the generator to produce electricity. The maximum voltage achieved is 25.7 volts at a steam pressure of 50 psi. The highest energy conversion efficiency was recorded at 4%, while the lowest efficiency was 0.9%. These findings demonstrate that, despite its relatively low efficiency, the prototype can function as an alternative energy source and emergency backup solution. Thus, this research provides an initial contribution to supporting the implementation of the V2G concept through the development of a small-scale steam plant-based energy conversion system.

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.

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.