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.

In the hold of the fishing boat, it is currently unable to control the pH of the water in accordance with the living environment of the fish. The pH of the water also needs to be considered so that the fish can survive. In addition, fish feeding on fishing vessels is also very necessary. The purpose of this research is to design a system that can control pH, fish feeding, fish water replacement. in the fish hold using an IoT-based microcontroller. The method used in this research uses quantitative methods through system design, design software design models, product trials.This method is used This research method includes steps taken to design and implement the system, as well as tests carried out to ensure the performance and success of the designed system. The results of the data designed by the system are analyzing the data in testing this tool according to its function. The accuracy of using the tool when feeding is 90.5% and the percentage error is 9.5%. The accuracy of using the tool when draining mode is 100% with a percentage error of 0%. And for the results of the percentage error of the pH sensor when the mode is on by 100% with a percentage error of 0%. This shows that the Smart Pond system designed has a high level of accuracy in terms of fish feeding and pond draining.