ANALISA DEBIT BANGKITAN MENGGUNAKAN MODEL ARIMA (AUTOREGRESIF INTEGRATED MOVING AVERAGE) (Universitas Muhammadiyah Palembang, 2022)
Flow discharge data must be available in a time series and accurate manner, so there should be no empty periods. Therefore, a model is needed that can reconstruct or estimate the flow discharge of the empty period stochastically. One way to solve this problem is by filling in data or data generation.The philosophy of data is to create new data sets based on generally incomplete or short historical data to obtain longer and more complete data. The new complete and long data is made with properties as well as short data as the source (Sri Harto and Sudjarwadi, 1989). Model ARIMA represents three modeling namely of autoregressive model (AR), moving average (MA), and autoregressive and moving average model (ARMA) which has characteristic of two models. First stage modeling ARIMA is testing stationary data, identification model, estimation parameter model and forecasting. Data used in this model Arima is discharge complete station Maribaya DAS Cikapundung Hulu if from years 1978 from the research. Results ARIMA model produces correlation values of 0.657 with a target value of 1. For the absolute relative error rate (KAR) and the average error rate (RMS) each produces a value of 0.0052 and 0.017 with a target value of 0. The ARIMA model can be used to fill in, generate discharge data and can be used to predict future flow rates. In discharge forecasting, the ARIMA model is only able to predict discharge accurately in a short time span. For long-term forecasting, the resulting forecast will tend to be flat (flat/constant).