Prediksi Kualitas Air Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA)

Abstract

Water conservation is very necessary to support the creation of clean water quality that is free from harmful substances that can disturb the environment. So a system is needed to monitor water quality to determine the level of pollution that occurs. This system will work to see water quality in real time with several quality parameters such as pH, temperature, and water turbidity. The purpose of this research is to produce a predictive model and find out the prediction results of a data mining-based system. The method used to predict water quality uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, because the water quality data is thought to contain seasonal patterns. The results of this study indicate that the SARIMA model can be applied to the dataset used and obtain the accuracy of the forecasting results on each of the tested parameter data. The results of water quality forecasting with this parameter are the result data for testing at a dataset of a depth of 30 cm and a depth of 60 cm for temperature parameters, namely MSE<0.1, and RMSE<0.02. For pH parameters, MSE<0.1, and RMSE<0.1. As well as the turbidity parameter, the results of MSE<0.02, and RMSE<0.13. From these results indicate that this system can predict water quality with past data.