TY - JOUR AU - Fahrin, Edy AU - Hayati, Memi Nor AU - Siringoringo, Meiliyani PY - 2020 TI - Penerapan Model Seasonal Autoregressive Fractionally Integrated Moving Average Pada Data Inflasi di Indonesia JF - EKSPONENSIAL; Vol 10 No 2 (2019) KW - N2 - Current inflation data is influenced by previous inflation data. Inflation data from time to time is indicated to have a long memory and seasonal pattern. The S easonal Autoregressive Fractional Integrated Mov i ng Average (SARFIMA ) model is one of the models used to predict data that has a lon g memory and seasonal pattern. The purpose of this research was to find out the the best SARFIMA model and forecast inflation in 2018 using the best SARFIMA model . The sample in this research was Indonesian monthly inflation data for the period January 2008 to December 2017 . There are f our stages of SARFIMA modeling, namely model identification, parameter estimation, diagnostic checking , and application of models for forecasting. Based on the results of the analysis, the best SARFIMA model selected based on the AIC and MSE criteria is the SARFIMA model with d = 0 . 687. The results of inflation forecasting from January to December 2018 show a fluctuating value every month with the inflation rate at 3 . 30 % - 3 . 65%. UR - https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/568