Peramalan Jumlah Penderita Campak Klinis Di Kota Surabaya Menggunakan Metode ARIMA - Kharis Putra Indrayatna (UNIVERSITAS MUHAMMADIYAH SURABAYA, 2019)
Objective: Forecasting is the process of making predictions of the future based on past, needed to determine when the occasion will occur,then the right action can be taken. ARIMA mehthod is a very strong aprroach model for time series analysis. ARIMA models can provide precise study. ARIMA models are very effective to applied in some cases where data show evidence of non-stationarity, shown by the ACF plot which drops exponentially. The ARIMA method is chosen because this model is suitable for all data types including stationary and non-stationary data. If the data is not stationer, where an initial differencing step can be applied to eliminate the non-stationarity. Measles is a highly contagious disease caused by a virus, 90% of children who are not immune can be affected by measles. Forecasting the number of measles sufferers in the city of Surabaya in 2018-2019 is very necessary to see the increasing trend of measles sufferers in the city of Surabaya.Methods: This type of research is non-reactive research. The population in this study is the total number of Clinical Measles sufferers recorded in the Surabaya City Health Office every month in the period between 2012 and 2017. The design of this study is a time series research design, the dependent variable is the number of measles sufferers, whereas the independent variable is time.Results: This study obtained ARIMA model forecasting for the next 2 years amounting to 407 cases in 2018 and 584 cases in 2019.Conclusion: The results of forecasting measles desease that happened in Surabaya have decreased in 2018, from 428 cases that occurred in 2017 to 407 cases, and can be estimated in 2019 to increase to 584 cases. The results of the forecasting can be used as a basis for running more specific programs based on problems.