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This study discusses the design and development of an automatic safety system for a wood cutting machine using Arduino Uno, a PIR (Passive Infrared) sensor, and a servo motor as the main components. The system is designed to automatically stop the movement of the wood cutting machine when human motion is detected around the cutting area, thereby minimizing the risk of work-related accidents. The research method includes hardware design, microcontroller programming, and system response testing using two types of test objects: the human body and a wooden block. The results show that the system operates according to the programmed logic. When the PIR sensor detects human motion, the servo motor stops and the red LED lights up as a danger indicator. In contrast, when no human motion is detected, the servo motor continues to move normally and the green LED remains on as a safe indicator. The system’s average response time is 0.6 seconds, indicating a fast and accurate performance. Therefore, the designed system is considered effective and can serve as a prototype of a simple safety tool to enhance operator safety in wood cutting machines.

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.

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 rise in motor vehicle theft cases in various regions indicates the weakness of the security systems implemented by most users. Systems such as manual locks and alarms often fail to prevent crime, either because they are easily hacked conventionally or due to user negligence in their operation. In today's technological era, a system is needed that is not only secure, but also intelligent and practical. One promising solution is the implementation of a facial recognition-based security system. This study aims to design and test a vehicle security simulation system using facial recognition technology integrated with Arduino Uno and MATLAB. This system utilizes a laptop camera to capture the user's facial image, then performs a detection and verification process using the FaceNet algorithm. If the face is recognized and verified with data stored in the database, the Arduino will activate the actuator components in the form of a DC motor to simulate starting the engine, and a servo motor to simulate opening the vehicle door. This study uses a quantitative experimental approach to analyze the effect of variations in distance (30, 40, and 50 cm) and lighting brightness levels (10–20, 21–30, and 31–40 lux) on the system's response time. A total of 27 combinations of conditions were tested, and the data obtained were analyzed using Microsoft Excel and ANOVA tests in Minitab software. The results of the analysis showed that the optimal response time was obtained at a distance of 40 cm with a medium level of illumination (21–30 lux). In addition, both distance, brightness, and the interaction between the two factors were shown to have a significant effect on the system's response time (P-Value < 0.05). These findings indicate that the system is quite sensitive to environmental changes, so further testing is highly recommended, especially to measure the actual delay, the detection error rate, and the development of a more robust face detection algorithm so that the system can be used reliably in various lighting conditions and face capture angles in the real world.

This research aims to be able to meet the water supply of lettuce plants automatically by using three sensors such as soil moisture, water level, and water discharge. The goal is to provide water needs to plants automatically and regularly. The developed tool uses YL-96 sensor for soil moisture, HC-SR04 for water level and YF-S201 for water discharge. Sensor data is sent to the arduino to be processed using the fuzzy mamdani method so that these three data values affect the movement of the tap servo motor that flows to the lettuce plant. Fuzzy logic here as a decision maker from the value of 3 sensor data and then processed automatically by arduino using fuzzy mamdani to determine how many degrees the servo motor moves. The result is that the Lettuce Plant Water Needs Analysis System Automation Tool is able to maintain the water supply of lettuce plants and soil moisture ideally at 76% with a servo motor movement system success rate of 100%.

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.

In recent decades, the use of unmanned vehicles (UV) has increased significantly. Unmanned ship operations involve various technical challenges that require sophisticated solutions. Dynamic marine environments, including strong currents, high waves, and extreme weather conditions, can affect ship performance to maintain stability and steer the ship properly, a precise and responsive control system is essential. Small errors in maneuvering can result in high risks, such as damage to the ship or loss of cargo, as well as potential danger to other ships or the environment. The purpose of this study is to design and build a servo motor control system for maneuvering systems on ships and to determine whether servo motors can control maneuvering systems on unmanned ships. The results of this study indicate that the servo motor control system in this study can work automatically or manually. In automatic mode, the motor angle can be set via the application. The ship will stop when the angle matches the settings in the application, based on data from the compass sensor. This sensor ensures that the direction of the ship's movement is accurate by providing a signal when the angle set matches the compass angle. The angle that has been determined when maneuvering the ship's automatic movement system is not accurate, due to currents, water waves and wind that make the ship move or change position by itself.

Overhead cranes are an important device in the manufacturing and construction industry that functions to move heavy loads efficiently and safely. However, human error often occurs which can cause work accidents, equipment damage, and decreased productivity. Therefore, this research aims to design and build an Arduino-based automatic overhead crane system that can increase work efficiency and safety. This system is designed using an Arduino microcontroller as a control center, equipped with an ultrasonic sensor to detect the position and distance of the load, as well as a servo motor to regulate crane movement automatically. The system also features a wireless communication module to monitor and control the crane remotely. The research method used is Research and Development (R&D), which includes the stages of needs analysis, system design, hardware and software implementation, and performance testing. Test results show that this automatic overhead crane is capable of moving loads with high accuracy and a speed that can be adjusted according to needs. The system also succeeded in reducing manual intervention, thereby increasing work safety and reducing the risk of accidents by up to 30% compared to manual systems. The Arduino-based automatic overhead crane design succeeded in meeting the research objectives, increasing work efficiency and safety. Routine maintenance and periodic checks on mechanical and electronic components need to be carried out to ensure the system continues to function optimally and reduces the risk of damage.

Floods are natural disasters that often occur in Indonesia and cause damage to property, infrastructure, and casualties. One of the main factors causing flooding is the failure of water management systems such as reservoirs to manage the sudden increase in water volume. This research aims to design an automatic reservoir sluice controller by utilizing Internet of Things (IoT) technology to reduce flood risk. The system uses a NodeMCU ESP8266 microcontroller and an HC-SR04 ultrasonic sensor to monitor the water level in real-time, as well as a servo motor that controls the sluices. The system is connected to the Blynk app, allowing remote monitoring and control of the sluice gates via mobile devices. This tool automatically adjusts the position of the sluice gate based on sensor data to keep the water level safe and reduce the potential for flooding. The test results show that this IoT-based automated system is able to work effectively in controlling sluice gates and can be used for flood disaster prevention in the surrounding environment.

Stroke is a physical disability disease, which affects the upper extremity motor system. To support post-stroke motor rehabilitation, research on IoT-based elbow exoskeleton therapy devices that help elbow therapy with flexion and extension movements. The therapy device uses DS-SERVORD5160 as the main driver to provide flexible movement. By using the ESP32 microcontroller control system equipped with a Bluetooth connection that is connected via the MIT App Inventor application from a smartphone that can be done at home to increase the effectiveness of post-stroke recovery. The results of this study with low (20°), medium (40 °) and high (60 °) modes by providing a maximum load of 2 Kg. It can be concluded that the device shows stable performance in low (20 °) and medium (40 °) modes, but in testing with high mode (60 °) with a load of 2 Kg there is a decrease in servo performance which is indicated by the instability of the servo movement which shows an angle of 56 °.

This research focuses on the design and implementation of an automatic parking barrier system using a microcontroller. The objective is to create an efficient and reliable system that automatically controls the parking barrier in response to vehicle detection. The system utilizes an infrared sensor for vehicle detection, which is processed by a microcontroller to control a servo motor for opening and closing the parking barrier. The research method includes simulation using Proteus software followed by physical implementation. Results show that the system operates efficiently with a quick response time of 1-2 seconds for barrier movement. The conclusion is that the system is effective in automating parking barrier control, reducing human error, and improving parking management. Further improvements can be made by enhancing sensor sensitivity and integrating additional technologies.  

This paper presents an IoT-enabled dual-axis solar tracking system that integrates  a Kalman filter and a Proportional-Integral-Derivative (PID) controller to enhance tracking accuracy, energy efficiency, and operational stability. Addressing the ongoing challenge of maxi- mizing photovoltaic (PV) panel output, the proposed system leverages an ESP32 microcontroller and the Blynk platform to provide real-time monitoring, remote parameter adjustments, and flexible connectivity. Light Dependent Resistor (LDR) sensors measure sunlight intensity from multiple directions, while MG90S servo motors dynamically adjust the panel’s azimuth and elevation. The Kalman filter refines noisy sensor data to yield precise sun position estimates, enabling the PID controller to respond quickly and accurately to deviations in panel orientation. Through extensive testing conducted over several days, including both clear and partially cloudy conditions, the system achieved an average Root Mean Square Error (RMSE) as low as 1.2° under clear skies and maintained RMSE below 2.0° even under partial shading. Compared to a fixed-panel baseline, daily energy harvesting improved by approximately 43%. These results confirm that advanced estimation and control algorithms, when combined with IoT functionali- ties, significantly outperform simpler tracking methods and static installations. Furthermore, the low-cost, compact design and user-friendly interface facilitate practical deployment in a range of scenarios, including small-scale and off-grid installations. By ensuring continuous alignment of the PV panel with the sun, the system not only increases overall energy capture but also reduces maintenance requirements through remote oversight. This research thus offers a robust, scalable approach to improving solar energy utilization in diverse and evolving environmental conditions.