Djalaluddin Gorontalo Airport Passenger Data Forecasting with Holt’s-Winters’ Exponential Smoothing Multiplicative Event-Based Method

Abstract

Abstract An appropriate time series model for predicting passenger data of Djalaluddin Gorontalo Airport, the interval between 2003 - 2017 with the multiplicative of Holt’s-Winters’ exponential smoothing as Y ^ t + p = ( L t + p T t ) S t − 12 + p , where for time t, the original exponential smoothing data, L t = 0.2 Y t S t − 12 + 0.8 ( L t − 1 + T t − 1 ) , smoothing trend patterns Tt = 0.2(Lt − L t−1) + 0.8T t−1 and smoothing seasonal patterns, S t = 0.2 Y t L t + 0.8 S t − 12 , so the smoothing parameter used are α = β = γ = 0.2. After several treatments, the mean square deviation (MSD) is 3287241 for arrival passenger data and MSD is 2490279 for passenger departure data with a seasonal length of 12 by do not event-based. The next, the MSD is 1334585 for arrival passenger data and MSD is 1433867 for passenger departure data with a seasonal length of 12 by Eid al-Fitr event-based, the MSD is 1259600 for arrival passenger data and MSD is 1252548 for passenger departure data with a seasonal length of 12 by Eid al-Adha event-based.