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The main problem with the water heating system on offshore platforms is the absence of water level monitoring and automatic overflow detection. This has the potential to cause hot water spills that endanger workplace safety and operational efficiency. This research designs and implements a water level monitoring system based on the Arduino Uno microcontroller with HC-SR04 ultrasonic sensors. The system is equipped with LED indicators, a buzzer alarm, and a 16x2 LCD to display water level status in real-time. Water levels are classified into three zones (low, medium, high), and overflow is detected if the water is within 3 cm of the sensor. Testing was conducted on a 5-liter simulation tank representing actual 500-liter tank conditions. Test results showed a reading accuracy of 96% and a quick system response to overflow conditions (<1 second). This system is economical, easy to develop, and highly applicable for offshore environments. In addition, this system can be integrated with IoT technology for remote monitoring.

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.

Unmanned Surface Vehicle (USV) is an unmanned vehicle that is currently being developed in Indonesian waters for the purposes of depth surveys in waters that combine autonomous navigation frames, environmental sensors, multibeam echosounders to collect topography, temperature, and other data that are utilized for the world of shipping. Researchers tested an unmanned ship tool that can move automatically and can determine the direction to a predetermined location. Output. The test results show that the unmanned ship can walk to the planned route. This unmanned ship is equipped with a Micro Air M10G GPS sensor, LoRa Sensor, Arduino Mega 2560 Pro Sensor... In testing as many as 15x experiments were carried out including 10x experiments carried out to turn right and 5x experiments with turning left. The experiments carried out were very effective and according to what had been planned, but there were 4x experiments that had differences from 11x experiments due to water waves and winds that were too large so that the ship was unstable.

Along with the development of the industrial sector, companies are competing to create quality and affordable products. To support production, automation tools have emerged that help the production process so that it is faster. One of the machines that is widely used by large factories is a 3-phase induction motor. 3-phase induction motors are often used in driving equipment in industry because they have a simple construction, are relatively cheap, lightweight, have high efficiency, and are easy to maintain. This study utilizes the A3114 hall effect sensor device, PZEM-PP4T Sensor, 20x4 LCD, ESP32 module, Relay, Mitsubishi D700, MCP 4725 Sensor. The tests carried out showed that the performance of the sensor for measuring speed, voltage, and current on 3-phase motors has a high level of accuracy. Testing of motor speed shows a difference in speed value (Rpm) between the hall effect sensor and the tachometer, with an average accuracy percentage of 4,12%, indicating that the hall effect sensor has a fairly good level of accuracy. For voltage measurement, the Pzem sensor compared to the Multitester produces an average accuracy percentage of 0,90%, indicating that the pzem sensor is very reliable. Meanwhile, current testing shows a difference in value between the pzem sensor and the multitester with an average accuracy percentage of 0,98%, indicating a fairly high level of accuracy. Overall, this system can be relied on for measuring operational parameters of 3-phase motors because it has consistent and good accuracy. With this research, the 3-phase induction motor monitoring system which was originally done manually has changed to automatic and has a fairly high level of work efficiency.

The need for urban communities to consume vegetables is increasing. This has caused people to start cultivating vegetables using hydroponic techniques. However, due to their busy activities, they do not have enough time to monitor and control hydroponics, which must always be in ideal conditions. This paper designs and implements an Internet of Things-based monitoring system to help hydroponic owners monitor their hydroponics anywhere and anytime. The built system requires a monitoring device assembled using a NodeMCU ESP8266 microcontroller, a pH detection detector sensor, and a DHT22 temperature and humidity sensor. This system uses the Mamdani Fuzzy Logic algorithm to determine warnings to be displayed on the application interface when the water pH, temperature, and humidity are in certain conditions. The Mamdani Fuzzy Logic algorithm can interpret environmental data into a warning that humans can easily understand, even if they lack technical expertise. In addition to being able to help monitor, this system also allows owners to find out what elements need to be added or changed for their hydroponic place. Our evaluation results show that the defuzzification stage in the application has high accuracy, which is 99.92%, compared to Matlab’s results.