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The occurrence of earthquakes is increasing almost every year in Indonesia. From January 2014 to December 2017, there was around 16,645 earthquakes with magnitude ≥4 Richter Scale occurred. This study is the first part of earthquake risk modeling that we conducted. This study aims to analyze the relationship of frequency and magnitude of the earthquakes by using Poisson Regression and Generalized Poisson Regression. The data used in this study is frequency and magnitude data of earthquakes occurred in Indonesia. The data were selected by selecting earthquakes with magnitude ≥4 Richter Scale in the period January 2014 to December 2017 (4 years). The dependent variable is frequency, meanwhile the magnitude is independent. The frequency of earthquakes is the rounded value of natural log (Ln) transformation of cumulative frequency of earthquakes occurred in time period, and tested that it follows poisson distribution. The Poisson Regression analysis was done for the first, then the analysis continued by using Generalized Poisson Regression to observe whether there is equidispersion effect. The result of two models was compared then continued by selecting the best model based on the smallest of Akaike Information Criterion (AIC). According to the result of Poisson Regression as well as Generalized Poisson Regression, the magnitude is significantly affect the frequency. Based on AIC, the best model of frequency-magnitude relationship is presented by Poisson Regression model, μ = exp (4.048 – 0.3935x). [ABSTRACT FROM AUTHOR]

Currency plays an important role in economic transactions of Indonesian society. In order to guarantee the availability of currency, Bank Indonesia needs to develop demand and supply planning of currency. The purpose of this study is to get model and predict inflow and outflow of currency in KPW BI Region IV (East Java) with ARIMA method, time series regression and ARIMAX. The data of monthly inflow and outflow is used of currency in KPW BI Surabaya, Malang, Kediri and Jember. The observation period starting from January 2003 to December 2014. Based on the smallest values of out-sample RMSE and SMAPE, ARIMA is the best model to predict the outflow of currency in KPW BI Surabaya and ARIMAX for KPW BI Malang, Kediri and Jember. The best forecasting model for inflow of currency in KPW BI Surabaya, Malang, Kediri and Jember chronologically as follows are calendar variation model, transfer function, ARIMA, and time series regression. These results indicates that the more complex models may not necessarily produce a more accurate forecast as the result of M3-Competition. [ABSTRACT FROM AUTHOR]

Bank Indonesia (BI) as the central bank of Republic Indonesiahas a single overarching objective to establish and maintain rupiah stability. This objective could be achieved by monitoring traffic of inflow and outflow money currency. Inflow and outflow are related to stock and distribution of money currency around Indonesia territory. It will effect of economic activities. Economic activities of Indonesians one of Moslem country, absolutely related to Islamic Calendar (lunar calendar), that different with Gregorian calendar. This research aims to forecast the inflow and outflow money currency of Representative Office (RO) of BI Semarang Central Java region. The results of the analysis shows that the characteristics of inflow and outflow money currency influenced by the effects of the calendar variations, that is the day of Eid al-Fitr (moslem holyday) as well as seasonal patterns. In addition, the period of a certain week during Eid al-Fitr also affect the increase of inflow and outflow money currency. The best model based on the value of the smallestRoot Mean Square Error (RMSE) for inflow data is ARIMA model. While the best model for predicting the outflow data in RO of BI Semarang is ARIMAX model or Time Series Regression, because both of them have the same model. The results forecast in a period of 2015 shows an increase of inflow money currency happened in August, while the increase in outflow money currency happened in July. [ABSTRACT FROM AUTHOR]