Forecasting for Book Classified on A Library by Using Single Exponential Smoothing (case Study : Library of Bhayangkara Surabaya University)

Abstract

To facilitate the addition of library collections of UBHARA libraries, in this study will provide a solution to field
manuals based on forecasting using MSE single exponential smoothing formula errors and RMSE errors. Data is
forecast from 2012 to 2016, with the value of each field of economics, law, socio-political, and engineering. The data
will be processed through the pre-processing process before preparing the data to be forecast. In the calculation
example, the program uses data in 2012 and 2015, alpha value = 0.1 and is calculated from month 1 to month to 3
months so it is estimated to 4. The result of the data obtained is borrowed book which has the highest data is
Economy. Because in every data the number of loan books looks more dominant economic data. In 2015 the
calculation shows the value of MSE error and RMSE error. The error value to determine whether the error
forecasting results is better or not. For 2015 forecast data to be displayed at the value of the error.