DAMPAK REDENOMINASI TERHADAP INFLASI INDONESIA: PENANGANAN MISSING MENGGUNAKAN METODE CASE DELETION, PMM, RF DAN BAYESIAN

  • Windri Wucika Bemi Politeknik Statistika STIS, Indonesia
  • Rani Nooraeni Politeknik Statistika STIS, Indonesia
Keywords: bayesian linier regression, missing data, predictive mean matching, random forest, redenomination

Abstract

Indonesia is the country with the third largest currency digit after Vietnam and Zimbabwe. In 2010, Indonesia conveyed a discourse on the application of rupiah redenomination, but in its implementation it was necessary to estimate the economic factors that would be affected, especially inflation, where inflation was one of the decisive indicators of the success of the redenomination policy of the currency. To estimate the impact of redenomination on inflation, Indonesia can reflect on the historical data of countries that have implemented the policy. Based on historical data, a model can be applied to Indonesia. Historical data includes macroeconomic variables and forms of government. To get a model with better precision, complete data needs to be considered. The historical missing will make the inferencing obtained invalid and important information that can be used for analysis also diminishes. The case deletion method, mean matching predictive, random forest, and bayesian linear regression can be used to handle it. The results showed that there were 38.18% missing data from total observations and the case deletion method as the best method. Then the condition of hyperinflation, economic growth, and the index of government forms significantly impacted inflation after the implementation of redenomination. So, if Indonesia applies redenomination between the period 2010-2017, with the classification accuracy of 64.71%, it is estimated that it will have a negative impact because the inflation will increase after redenomination is implemented.

Published
2019-10-31
Section
Articles