Utilization of Holt's Forecasting Model for Zakat Collection in Indonesia

Abstract

The practice of zakat is gaining popularity in Indonesia. This development is attributed to the strong role of the government in consistently developing zakat infrastructure and the increased awareness of people to practice zakat. Despite this success, a mechanism for predicting future zakat collection has not yet been developed. This study applies Holt's exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) model to forecast zakat in Indonesia using zakat collection from 2009 to 2014. Results show that Holt's exponential smoothing is best fits the zakat time series data and is therefore suitable for forecasting zakat. Holt's exponential smoothing is comparable to the ARIMA model given its small deviations in mean absolute percentage error and mean square error. Moreover, the software used to implement Holt's exponential smoothing is similar to that used in ARIMA models. These similarities show that these models can accurately forecast future trends to prepare proper strategies and plan the future of the organization. These models can also be used to develop a plan for managing charity based on the number of recorded mustahiq.