| dc.creator |
Rajabu, Yusuph |
|
| dc.date |
2021-01-28T08:48:12Z |
|
| dc.date |
2021-01-28T08:48:12Z |
|
| dc.date |
2020 |
|
| dc.date.accessioned |
2022-10-20T13:46:59Z |
|
| dc.date.available |
2022-10-20T13:46:59Z |
|
| dc.identifier |
Rajabu, Y. (2020). Prediction of annual revenue collection by using data mining techniques: a case of local government authorities in Tanzania (Master's Dissertation). The University of Dodoma, Dodoma. |
|
| dc.identifier |
http://hdl.handle.net/20.500.12661/2697 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.12661/2697 |
|
| dc.description |
Dissertation (MSc Information System) |
|
| dc.description |
Local Government Authority have to pay increasing attention to the importance and need of annual revenue prediction due to financial, economic and political stress. Currently, judgmental models are used for LGAs revenue prediction with poor accuracy. Due to increasing importance; the aim of this study is to develop a model for predicting annual revenue collection of LGAs in Tanzania with the help of agricultural weather condition, exchange rate, national GDP, Council population, number of council enterprise, previous annual collection, and physical person income tax by using support vector regression. The data used for this paper was from 1ST July 2009 to 30 June 2019 hardly ten year data. Support vector regression and artificial neural networks are the algorithm which are used for predicting because of their competences of pattern recognition and machine learning. In this study the two algorithm ANN and SVR were used to develop a model for predicting the annual revenue collection for LGAs and their performance has been compared for evaluation so as to get the best performer. According to the results there are high similarities between predicted and actual data for both SVR and ANN. Predicted results of this study shows that SVR score 94.2% model accuracy as compared to 85% model accuracy of ANN. Because of this high accuracy and outperforming of SVR, LGAs in Tanzania can be able to apply SVR model as a revenue predictive tool in upcoming fiscal year and able to bridge a gap between revenue predicted versus actual revenue collection. |
|
| dc.language |
en |
|
| dc.publisher |
The University of Dodoma |
|
| dc.subject |
Local Government Authority |
|
| dc.subject |
LGA |
|
| dc.subject |
Annual revenue |
|
| dc.subject |
Political stress |
|
| dc.subject |
Revenue prediction |
|
| dc.subject |
Annual revenue collection |
|
| dc.subject |
Exchange rate |
|
| dc.subject |
Data mining techniques |
|
| dc.subject |
Vector regression |
|
| dc.subject |
Artificialneural networks |
|
| dc.subject |
Revenue Projection |
|
| dc.title |
Prediction of annual revenue collection by using data mining techniques: a case of local government authorities in Tanzania |
|
| dc.type |
Dissertation |
|