Real-Time Monitoring of Pond Water Quality Parameters with Iot to Determine Optimum Data Using Euclidean Distance Algorithm

Abstract

The objective of this research is to develop an Internet of Things (IoT) system that is capable of monitoring shrimp pond water quality in real-time, with a focus on three main parameters: temperature, pH, and dissolved oxygen (DO).The system integrates hardware in the form of sensors and microcontrollers, as well as cloud-based software for data processing and visualization. The collected data underwent analysis using the Euclidean Distance and Weighted Euclidean Distance methods to calculate the distance between data, followed by a filtering process to filter relevant data based on proximity.The findings of the study demonstrate that the IoT system is capable of measuring water quality parameters with good accuracy and of automatically transmitting data to the cloud platform for further monitoring and analysis.The Weighted Euclidean Distance method provides more optimal results than standard Euclidean Distance, because it considers the weight of each parameter. It is hoped that this system can provide an innovative solution to support more efficient, data-based and sustainable shrimp pond management.