Forecasting Zakat Collection In Malaysia Using Time Series Analysis

Holt-Winters and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models are used to predict monthly zakat collection in Lembaga Zakat Selangor (LZS), Pusat Zakat Negeri Sembilan (PZNS), and Pusat Pungutan Zakat (PPZ) using zakat collection data from January 2010 to December 2019. Nonseas...

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Main Author:	Mohd Fadlihisyam bin Ishak
Format:	Thesis
Language:	en_US
Subjects:	Zakat > Malaysia Zakat > Management Donation (Islamic law)
Online Access:	https://oarep.usim.edu.my/bitstreams/4f86c31d-28f5-4513-8d31-2bdabde5b3cc/download https://oarep.usim.edu.my/bitstreams/a71db699-1412-421b-9fbe-8be80e828a94/download https://oarep.usim.edu.my/bitstreams/a8671090-0e41-4a42-9c61-e4e236a48db4/download https://oarep.usim.edu.my/bitstreams/5655bd99-6c60-489d-b493-b485d3f18417/download https://oarep.usim.edu.my/bitstreams/2d2914a3-c532-4bcf-b4aa-61db32d050d4/download https://oarep.usim.edu.my/bitstreams/cd993ce8-2d81-4b3d-a016-d407d2bea93b/download https://oarep.usim.edu.my/bitstreams/c4586f72-0c7a-4f90-a764-2f6cbc7bc561/download https://oarep.usim.edu.my/bitstreams/436de9af-36a7-4598-aa90-5b652d48fa76/download https://oarep.usim.edu.my/bitstreams/a530ba9f-4d71-4c30-af30-28c91f033772/download
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id	my-usim-ddms-12513
record_format	uketd_dc
institution	Universiti Sains Islam Malaysia
collection	USIM Institutional Repository
language	en_US
advisor	Dr. Asmah Binti Mohd Jaapar
topic	Zakat--Malaysia Zakat -- Management Donation (Islamic law) Zakat--Malaysia
spellingShingle	Zakat--Malaysia Zakat -- Management Donation (Islamic law) Zakat--Malaysia Mohd Fadlihisyam bin Ishak Forecasting Zakat Collection In Malaysia Using Time Series Analysis
description	Holt-Winters and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models are used to predict monthly zakat collection in Lembaga Zakat Selangor (LZS), Pusat Zakat Negeri Sembilan (PZNS), and Pusat Pungutan Zakat (PPZ) using zakat collection data from January 2010 to December 2019. Nonseasonal models such as the Auto-Regressive Integrated Moving Average (ARIMA) and the Single Smoothing Exponential are used to predict yearly zakat collections in Majlis Agama Islam dan Adat Melayu Perak (MAIPk) using zakat collection data from year 1991 to 2019. In this research, we compare the forecasted values of both models and select the best model based on the least Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The objective of this study is to find the best model for forecasting zakat collection for a zakat institution. According to the results obtained using MSE, RMSE, MAE, and MAPE, the ARIMA(1,1,1) (1,1,1)12 and ARIMA(0,1,1) (0,1,1)12 models were found to be a better model for PZNS and PPZ, respectively. The ARIMA(1,1,1) (1,1,1)12 was found to be a better model for LZS based on MSE error. The ARIMA model was found to be the best fit for MAIPk and could be used to forecast future values from 2020 to 2031. The study shows that these models can accurately predict future zakat collection to prepare the appropriate strategies and plan for zakat distribution without leaving any surplus. These models can also be used to create a strategy to handle zakat funds based on the amount of asnaf registered.
format	Thesis
author	Mohd Fadlihisyam bin Ishak
author_facet	Mohd Fadlihisyam bin Ishak
author_sort	Mohd Fadlihisyam bin Ishak
title	Forecasting Zakat Collection In Malaysia Using Time Series Analysis
title_short	Forecasting Zakat Collection In Malaysia Using Time Series Analysis
title_full	Forecasting Zakat Collection In Malaysia Using Time Series Analysis
title_fullStr	Forecasting Zakat Collection In Malaysia Using Time Series Analysis
title_full_unstemmed	Forecasting Zakat Collection In Malaysia Using Time Series Analysis
title_sort	forecasting zakat collection in malaysia using time series analysis
granting_institution	Universiti Sains Islam Malaysia
url	https://oarep.usim.edu.my/bitstreams/4f86c31d-28f5-4513-8d31-2bdabde5b3cc/download https://oarep.usim.edu.my/bitstreams/a71db699-1412-421b-9fbe-8be80e828a94/download https://oarep.usim.edu.my/bitstreams/a8671090-0e41-4a42-9c61-e4e236a48db4/download https://oarep.usim.edu.my/bitstreams/5655bd99-6c60-489d-b493-b485d3f18417/download https://oarep.usim.edu.my/bitstreams/2d2914a3-c532-4bcf-b4aa-61db32d050d4/download https://oarep.usim.edu.my/bitstreams/cd993ce8-2d81-4b3d-a016-d407d2bea93b/download https://oarep.usim.edu.my/bitstreams/c4586f72-0c7a-4f90-a764-2f6cbc7bc561/download https://oarep.usim.edu.my/bitstreams/436de9af-36a7-4598-aa90-5b652d48fa76/download https://oarep.usim.edu.my/bitstreams/a530ba9f-4d71-4c30-af30-28c91f033772/download
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spelling	my-usim-ddms-125132024-05-29T20:14:10Z Forecasting Zakat Collection In Malaysia Using Time Series Analysis Mohd Fadlihisyam bin Ishak Dr. Asmah Binti Mohd Jaapar Holt-Winters and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models are used to predict monthly zakat collection in Lembaga Zakat Selangor (LZS), Pusat Zakat Negeri Sembilan (PZNS), and Pusat Pungutan Zakat (PPZ) using zakat collection data from January 2010 to December 2019. Nonseasonal models such as the Auto-Regressive Integrated Moving Average (ARIMA) and the Single Smoothing Exponential are used to predict yearly zakat collections in Majlis Agama Islam dan Adat Melayu Perak (MAIPk) using zakat collection data from year 1991 to 2019. In this research, we compare the forecasted values of both models and select the best model based on the least Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The objective of this study is to find the best model for forecasting zakat collection for a zakat institution. According to the results obtained using MSE, RMSE, MAE, and MAPE, the ARIMA(1,1,1) (1,1,1)12 and ARIMA(0,1,1) (0,1,1)12 models were found to be a better model for PZNS and PPZ, respectively. The ARIMA(1,1,1) (1,1,1)12 was found to be a better model for LZS based on MSE error. The ARIMA model was found to be the best fit for MAIPk and could be used to forecast future values from 2020 to 2031. The study shows that these models can accurately predict future zakat collection to prepare the appropriate strategies and plan for zakat distribution without leaving any surplus. These models can also be used to create a strategy to handle zakat funds based on the amount of asnaf registered. Universiti Sains Islam Malaysia 2022-03 Thesis en_US https://oarep.usim.edu.my/handle/123456789/12513 https://oarep.usim.edu.my/bitstreams/d62f7aab-67a4-4399-8479-e59936ada0c3/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/4f86c31d-28f5-4513-8d31-2bdabde5b3cc/download 62aeeed8e6a1c0500a72dbe3ee9d5118 https://oarep.usim.edu.my/bitstreams/a71db699-1412-421b-9fbe-8be80e828a94/download ba7970e78cb5efadfeaeeb6d7bfdfa20 https://oarep.usim.edu.my/bitstreams/a8671090-0e41-4a42-9c61-e4e236a48db4/download 23d7bb6622448bff27697d19e4fc836f https://oarep.usim.edu.my/bitstreams/5655bd99-6c60-489d-b493-b485d3f18417/download e9f67b00ca3edaf0b8e00d3a591b4669 https://oarep.usim.edu.my/bitstreams/2d2914a3-c532-4bcf-b4aa-61db32d050d4/download c445771848f4c11a325f9b4e46909248 https://oarep.usim.edu.my/bitstreams/cd993ce8-2d81-4b3d-a016-d407d2bea93b/download 3ffc8608828e08342fc8420a065a92d1 https://oarep.usim.edu.my/bitstreams/c4586f72-0c7a-4f90-a764-2f6cbc7bc561/download 8824b4ef187c503d36220e94042b76eb https://oarep.usim.edu.my/bitstreams/436de9af-36a7-4598-aa90-5b652d48fa76/download 6714e207fa68fc79a5dad09b91831942 https://oarep.usim.edu.my/bitstreams/a530ba9f-4d71-4c30-af30-28c91f033772/download cabed886c72d97c470181754065c9d44 https://oarep.usim.edu.my/bitstreams/81f02309-5069-4f30-898c-753760d5f6a3/download 68b329da9893e34099c7d8ad5cb9c940 https://oarep.usim.edu.my/bitstreams/b17be191-7512-4d33-a631-d1a2514ac3c1/download bf327b1ec6ca9c4d155aff4ea36e2036 https://oarep.usim.edu.my/bitstreams/e8e7acdc-1357-471f-9b02-1345e3aba511/download 5d526654e186cec332bd9ac5bda6ff5b https://oarep.usim.edu.my/bitstreams/3198b33f-a399-492a-b4da-10de3142f33d/download 4d7a0cc234b36c8053099cb99b9acddb https://oarep.usim.edu.my/bitstreams/efbf51b4-fdcc-4b05-b343-be9b8cfbee37/download f62acc4b2233e159f06d0d2485c6d73c https://oarep.usim.edu.my/bitstreams/eb1c66d7-42a3-4a6d-9e02-0e6a8b2eea85/download ba6f2c19fc0d233cbfcac2774aad3a59 https://oarep.usim.edu.my/bitstreams/81263738-5b45-4a75-ac6b-74b9ed06b889/download ee88992f724733dd8b3f19635e11767b https://oarep.usim.edu.my/bitstreams/635703e3-4788-4dc5-af92-786819e4fdf4/download 8f8dfe95cf8d201e1b696be23f319c46 https://oarep.usim.edu.my/bitstreams/cc1d517d-4f79-452a-bc3a-504ed8fad99a/download 9e4a9fb950329039a080df399b8b0e23 Zakat--Malaysia Zakat -- Management Donation (Islamic law) Holt-Winters, SARIMA, ARIMA, Single Exponential Smoothing, Forecasting, Zakat Trend, Zakat

Forecasting Zakat Collection In Malaysia Using Time Series Analysis

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