Utilisation of Holt-Winters Forecasting Model in Lembaga Zakat Selangor (LZS) For Zakat Collection

Abstract

Predicting the collection of zakat in Malaysian zakat institutions is crucial for effective zakat distribution. The surplus problems in zakat funds motivated this study to use more precise statistical methods to predict the future trend of zakat collection. The main objective of this paper is to forecast monthly zakat collection for 12 months ahead of the Lembaga Zakat Selangor (LZS). This research used the Seasonal Exponential Smoothing (Holt-Winters) model to predict zakat collection in LZS. The study utilised monthly zakat collection time series data from 2010 to 2018. The analysis was carried out using Excel Solver. The findings show that the Holt-Winters model is suitable to forecast the monthly zakat collection of LZS as it accounts for seasonal variation. The finding of this study indicates that the Holt-Winters Multiplicative (HWM) model best fits the monthly zakat collection time series data. The multiplicative form of Holt-Winters model yields 24.51% lower error compared to the additive one using the Mean Absolute Percentage Error (MAPE). The findings of this study will help zakat institutions to accurately predict future zakat collection which may consequently improve the management of zakat distribution without leaving a significant amount of zakat surplus. The forecast results can also be used to create a strategy to handle zakat funds based on the amount of registered asnaf. In addition, the study can serve as a basis for the development of a framework to forecast future zakat collections.

Using Holt Winter 2 Variable Modelling To Analyze The Potential Combining Of Zakat Collection In Three Countries In Southeast Asia As One Business Centre

“…The study of zakat modeling has grown significantly in Indonesia as well, particularly for the aim of forecasting zakat's worth in the future. According to a recent study that forecasted zakat, Holt's Exponential Smoothing (HES) model is a better choice for forecasting zakat data in Indonesia (Akbarizan, et al, 2016;Ilyas, et al, 2017;Ishak & Jaapar, 2020).…”