Publication Search

Sea water pollution is an increasingly urgent global issue, caused by various factors such as industrial waste, household waste, and ship activities. One of the solutions needed to overcome this pollution is the development of a detection system that is able to monitor polluting substances quickly, accurately, and efficiently. This research aims to design and build a microcontroller-based seawater pollution detection system, which can identify various pollution parameters in real-time. This research uses the Research and Development (R&D) method to develop a system consisting of several main components, including a pH sensor, turbidity sensor, Arduino Uno, GPS module, Raspberry Pi 4, USB camera, LiPo battery, and step-down converter. Each component is tested individually before being integrated into the overall system. The results of testing in a real environment show that the system is able to detect seawater pollution parameters with high accuracy. However, there are some errors in data collection, especially in the camera sensor with a percentage error of 32%, turbidity sensor 20%, and pH sensor 24%. Further improvements and developments were made based on the evaluation results to enhance system performance. The resulting system is considered accurate, reliable and easy to use, making an important contribution to efforts to protect seawater quality and mitigate the negative impacts of pollution on the environment and human health.

The storage space for CO2 gas cylinders is inside the ship's accommodation, so there is a risk of danger if a leak occurs because the ship's accommodation has poor air circulation. This research is devoted to removing dangerous gases from the room. This research designs and modifies a tool that can detect CO2 levels and can provide a danger signal to the surroundings. This modification uses a Raspberry Pi Pico W microcontroller. This research method uses system design, a series of tools with wiring design and uses 2 test plans, namely: testing static and dynamic testing.  Testing the precision of the sensor by reading the CO2 leak detector using a CO2 measuring instrument obtained an average error value of 3.5%. The error value is still categorized as a safe difference value and the prototype works according to the expected function. Testing the function of the tool results in values ​​for the MQ-135 buzzer and exhaust fan sensors which will turn on if the CO2 level is above 600 ppm in accordance with the safe threshold for CO2 levels in the air which has been set in the program. Telegram request bot testing can work well as expected. Testing the Telegram Receive bot can work well as expected. However, for testing Telegram bots, requests and receives are influenced by the internet network.

Research on weather elements is very necessary for the benefit and welfare of mankind, the observed weather elements will become material for predicting the weather in the future. This data is also used to prevent the risk of adverse impacts caused by the weather itself. To measure the weather with this automatic system, a tool called AWS is needed. The AWS design requires a Rasberry Pi. By using this Rasberry Pi we can monitor weather changes originating from BMKG via the web in the form of an Analog Gauge.

The Dental Unit consists of a patient chair, lights, machines and equipment or other equipment accessories needed during surgery to directly examine the inside of the teeth. Doctors usually use a curved mirror to be able to see the inside of the patient's teeth. This will be quite troublesome for doctors because they will not be free to check the condition of the patient's teeth. So in this research, we intend to design a tool that can be used to examine patients' teeth using a camera equipped with a wireless connection on a PC so that the results of the doctor's examination can be seen on the monitor screen. Measurement point 1 (TP1) of the PLN network with an average of 221.68 and an error value of 0.76%. Measurement point 2 (TP2) Rasberry power supply with an average of 4.61 and an error value of 8%. Measurement point 3 (TP3) keyboard with an average of 4.73 and an error value of 7%. Measurement point 4 (TP4) dental camera with an average of 4.53 and an error value of 9.3%. Measurement point 5 (TP5) rasberry fan cooler with an average of 5.05 and an error value of 1%. In testing the function of the camera module in the dental unit, the author will present images taken when testing the function of the device, when the camera is inserted into the patient's mouth and produces good images. measurements performed by researchers on monitor resources