Analisis Data Ketinggian Permukaan Air Sungai Mahakam Daerah Kutai Kartanegara Tahun 2010-2016 Menggunakan Model Autoregressive Integrated Moving Average (ARIMA) Dengan Efek Outlier: Studi Kasus: Data Rata-rata Ketinggian Tiap Bulan Permukaan Air Sungai Mahakam, Tenggarong, Kalimantan Timur - Agustianto, Rezky; Purnamasari, Ika; Suyitno, Suyitno (Program Studi Statistika FMIPA Universitas Mulawarman, 2020)
Measurement of water level is useful as a guide for flood events in an area. As a result of global warming it is predicted that rainfall will increase and the water level will be high, so that the chances of flooding will increase. The method often used in forecasting is the method of Autoregressive Integrated Moving Average (ARIMA). ARIMA is one of the time series forecasting methods that has been studied in depth by Box and Jenkins. ARIMA's basic concepts include,identification of models, parameter estimation, diagnostic checks and forecasting. Forecasting results with the ARIMA method are inaccurate, on data that contains outliers. The weakness of the ARIMA method can be overcome using the ARIMA method with outlier detection. The type of outlier detection in this study is additive outlier (AO). The purpose of this study was to determine the ARIMA forecasting model with an outlier effect on the average water level data of the Mahakam River in the Kutai Kartanegara Region in front of the Tenggarong Museum Building from January 2010 - December 2016. The results showed that the best forecasting model was the river Mahakam Kutai Kartanegara Region is ARIMA ([12], 1,0) with the addition of 4 outlier effects and measure of goodness is AIC with a value of 250,0776.