Research aims to design and develop a boiler system for heating Marine Fuel Oil (MFO) 180 CST by integrating Internet of Things (IoT) technology to enhance efficiency and operational monitoring. The methods used include boiler system design, selection of types and materials according to international standards, and the implementation of an optimal combustion system. IoT sensors are strategically placed to monitor key parameters such as temperature, pressure, and fluid flow in real-time. The collected data is transmitted to a cloud platform, enabling remote monitoring and automated performance analysis through a web or mobile-based application.. The research results indicate that IoT integration in the boiler system improves fuel heating efficiency, optimizes energy consumption, and facilitates easier monitoring and process control. Testing includes pressure tests, combustion efficiency, steam capacity, and material durability, with real-time monitoring to support performance analysis and early problem detection. Operational data evaluation allows for design adjustments or system settings to further enhance energy efficiency. With this innovation, the boiler system can operate more optimally, support energy efficiency, and facilitate predictive maintenance for sustainable industrial operations. The implementation of IoT in this system is expected to improve safety, effectiveness, and automation in boiler management, making it a more reliable and modern solution.

Automatic Identification System (AIS) is a communication technology that plays an important role in improving operational safety and efficiency in the shipping industry. AIS allows ships to exchange real-time data on identity, position, speed, and direction, which helps prevent collisions and facilitates maritime traffic management by port authorities. In addition, AIS functions in search and rescue operations by providing accurate information on the location of ships in trouble. In terms of security, AIS allows monitoring of suspicious ships, thus helping in preventing illegal activities in the waters. This study aims to design and develop a prototype AIS receiver based on LoRa, Arduino, and LCD HMI. The LoRa module was chosen because of its ability to transmit data over long distances with low power consumption, which is suitable for the maritime environment. Arduino is used as the main microcontroller to control the system, while the LCD HMI serves as the display interface for the received data. After the hardware and software design was completed, the system was tested through functional testing and performance measurements using a spectrum analyzer to evaluate the strength of the LoRa signal at various distances. The test results show that the AIS receiver is able to receive data well up to 15 meters on land and 13 meters at sea, with a delay of 100 milliseconds. System performance degrades at longer distances due to environmental interference and signal attenuation. These findings provide insight into the effective limits of LoRa communication in maritime applications and can be used as a reference for frequency testing and optimization of LoRa-based long-range communication systems.

The Automatic Identification System (AIS) is a maritime identification and tracking system that must be implemented on certain vessels in accordance with the regulations of the International Maritime Organization (IMO). One of the ways to enhance maritime safety on ships is by utilizing AIS. This research aims to develop a low-cost AIS intended for use on small vessels such as fishing boats. This study utilizes hardware components such as the GPS M8N, Arduino Nano, and LoRa RA-02 module. The LoRa module is chosen for its ability to transmit data over long distances with low power consumption, making it ideal for maritime applications. The Arduino microcontroller is responsible for controlling the entire system, while the GPS M8N is highly effective in achieving accurate positioning within meter-level precision. The Arduino Nano serves as the main component for receiving and processing data from the GPS M8N and LoRa RA-02, then transmitting the data to the receiver. The methodology and system design in this research follow an experimental approach, where at least one variable is manipulated to study cause-and-effect relationships. The test results of the AIS transmitter device indicate that it can successfully transmit call sign, latitude, and longitude data, with a maximum transmission range of 15 meters on land and 13 meters at sea.

The phenomenon that occurs in the load lifting system is the need for an efficient and reliable mechanism to move the crane hoist pulley vertically and horizontally. This study aims to design and test a crane hoist system that uses a DC motor and a stepper motor controlled by an L293D motor driver, and to develop an application based on MIT App Inventor to control the movement of the motor. The research method used is integrated hardware and software design, followed by testing the performance of the motor, driver, and the application developed. Testing was carried out under limited load conditions, to measure the motor speed, voltage, and current used at loads of 10 to 100 grams. The results of the study showed that the system can function well at varying speeds, and the motor is able to lift a maximum load of up to 100 grams with a speed that decreases according to the weight of the load. The MIT App Inventor application successfully controls the motor effectively via a WiFi connection with a maximum distance of 10 meters, and loads the operation of the crane vertically and horizontally. This study proves that the designed system is able to work optimally in lifting and moving loads, and the application developed provides easy and efficient control.

The Unmanned ships have become a primary focus in maritime technology development to enhance efficiency and navigational safety in waters. In this research, a unmanned ship utilizing ESP32CAM as its core system is designed to detect navigation lights of other vessels and avoid potential collisions. The initial response of the ESP32CAM camera reads the colors of lights approaching the unmanned ship within a distance of less than 100cm. The ESP32CAM reads and identifies colors through image processing. When the ESP32CAM detects green and red lights, it commands the buzzer to sound. The color detection system works such that when the ESP32CAM detects a red light, it sends a signal to the servo motor to move left, and when it detects a green light, it sends a signal to the servo motor to move right. The servo motor functions as the ship's rudder. Testing of the navigation light system with ESP32CAM camera to prevent ship collisions is conducted with predetermined scenarios, including testing with red LEDs, green LEDs, and overall system testing. The results show that the navigation light system with ESP32CAM camera functions well. The ESP32CAM camera can optimally identify light colors at a distance of 100cm. Based on the conducted research, the ESP32CAM camera is capable of reading both the distance and colors of lights as specified at a distance of 100cm.