Implementing the Adaptive Response Rate Single Exponential Smoothing Method to Estimate New Students at BiMBA AIUEO Jambon
Abstract
Purpose: Implementing the Adaptive Response Rate Single Exponential Smoothing (ARRSES) method using the initialized parameters (alphaand beta) on small-scale data values. Design/methodology/approach: Using Adaptive Response Rate Single Exponential Smoothing to estimate new trial students over a month. Findings/result: the method works on small-scale data values; however, the MAPE value shows a significant level of accuracy with a slight differentiation on parameter and data values. The highest MAPE value is 6.8% which is considered high accuracy in April 2019, with only 1 deviation between real and estimated data using parameter 0,1. Whereas the lowest MAPE value is 177% appraised as weak accuracy with 9 deviations, and using 0.2 as the parameter value. Originality/value/state of the art: The variance in this research is based on the data type and object. This research data is absolute (people as an object), then analyzed using past data from previous periods to estimate the probability of an entrant student in the month hereafter.
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