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Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)

Royal Customs Department Malaysia (RCDM) has the main function to collect revenue through indirect taxes, instead of giving facilitation and encourage industrialization and trades according to current provisions by laws and regulations towards vision 'to be recognized respected and world class...

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Main Author: Mohd Afandi, Md Amin
Format: Thesis
Language:English
English
Published: 2004
Subjects:
QA76 Computer software
Online Access:http://etd.uum.edu.my/1320/1/MOHD._AFANDI_MD._AMIN.pdf
http://etd.uum.edu.my/1320/2/1.MOHD._AFANDI_MD._AMIN.pdf
http://etd.uum.edu.my/1320/
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spelling my.uum.etd.13202013-07-24T12:11:26Z http://etd.uum.edu.my/1320/ Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP) Mohd Afandi, Md Amin QA76 Computer software Royal Customs Department Malaysia (RCDM) has the main function to collect revenue through indirect taxes, instead of giving facilitation and encourage industrialization and trades according to current provisions by laws and regulations towards vision 'to be recognized respected and world class services'. RCDM was setting revenue collection target yearly along with Strategic Planning 2001-2005. The revenue collection target has become an important agenda for every State Director of Customs through nationwide. Every state has to set their collection target based on the capability and tax resources, licensees and current performance. Forecasting revenue collection used statistical fundamental model has been used by RCDM by the year 2002 via deployment completed forecasting software that recognized as Forecast Pro Version 4.0. The significant of revenue forecasting is it provided a method to monitor collection performance and to take effective planning in order to ensure that revenue collection target can be achieved. This is as a result of the revenue collection performance was depends on various factors such as economics, politics, government policy and business environment that always been changing. This study is more on a new exploration technique using Artificial Neural Network (ANN) towards forecasting revenue collection of RCDM. The data sets were gathered from Monthly Revenue Return that provided and allowed to be used by Technique Division, RCDM, Alor Setar, Kedah. The data sets comprises of 1727 data that composed by 7 types of the duty and tax with non tax revenue from 159 successive month starting from 1st January, 1990 to 30 Mac, 2004. ANN with back propagation model, MLP has been used to train data sets in order to develop forecasting model revenue collection. Hopefully this study can be assist RCDM to develop a complete forecasting revenue collection tools using ANN for future enhancement. This study has proven the capability and reliability of ANN towards forecasting revenue collection. The results from BP model have proven the accuracy of forecasting is more than 92 percent. The ANN was found that can feed the data towards forecasting revenue collection. Thus, making is faster and easy to use. On the other hand, this study also adds more study domain related to current ANN applications for Faculty of Information Technology, Northern University of Malaysia. 2004 Thesis NonPeerReviewed application/pdf en http://etd.uum.edu.my/1320/1/MOHD._AFANDI_MD._AMIN.pdf application/pdf en http://etd.uum.edu.my/1320/2/1.MOHD._AFANDI_MD._AMIN.pdf Mohd Afandi, Md Amin (2004) Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP). Masters thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Mohd Afandi, Md Amin
Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)
description Royal Customs Department Malaysia (RCDM) has the main function to collect revenue through indirect taxes, instead of giving facilitation and encourage industrialization and trades according to current provisions by laws and regulations towards vision 'to be recognized respected and world class services'. RCDM was setting revenue collection target yearly along with Strategic Planning 2001-2005. The revenue collection target has become an important agenda for every State Director of Customs through nationwide. Every state has to set their collection target based on the capability and tax resources, licensees and current performance. Forecasting revenue collection used statistical fundamental model has been used by RCDM by the year 2002 via deployment completed forecasting software that recognized as Forecast Pro Version 4.0. The significant of revenue forecasting is it provided a method to monitor collection performance and to take effective planning in order to ensure that revenue collection target can be achieved. This is as a result of the revenue collection performance was depends on various factors such as economics, politics, government policy and business environment that always been changing. This study is more on a new exploration technique using Artificial Neural Network (ANN) towards forecasting revenue collection of RCDM. The data sets were gathered from Monthly Revenue Return that provided and allowed to be used by Technique Division, RCDM, Alor Setar, Kedah. The data sets comprises of 1727 data that composed by 7 types of the duty and tax with non tax revenue from 159 successive month starting from 1st January, 1990 to 30 Mac, 2004. ANN with back propagation model, MLP has been used to train data sets in order to develop forecasting model revenue collection. Hopefully this study can be assist RCDM to develop a complete forecasting revenue collection tools using ANN for future enhancement. This study has proven the capability and reliability of ANN towards forecasting revenue collection. The results from BP model have proven the accuracy of forecasting is more than 92 percent. The ANN was found that can feed the data towards forecasting revenue collection. Thus, making is faster and easy to use. On the other hand, this study also adds more study domain related to current ANN applications for Faculty of Information Technology, Northern University of Malaysia.
format Thesis
author Mohd Afandi, Md Amin
author_facet Mohd Afandi, Md Amin
author_sort Mohd Afandi, Md Amin
title Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)
title_short Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)
title_full Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)
title_fullStr Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)
title_full_unstemmed Towards Forecasting Revenue Collection Using Multi Layer Perceptron (MLP)
title_sort towards forecasting revenue collection using multi layer perceptron (mlp)
publishDate 2004
url http://etd.uum.edu.my/1320/1/MOHD._AFANDI_MD._AMIN.pdf
http://etd.uum.edu.my/1320/2/1.MOHD._AFANDI_MD._AMIN.pdf
http://etd.uum.edu.my/1320/
_version_ 1644276414437916672
score 13.211869

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