research-article

Revenue Prediction for Malaysian Federal Government Using Machine Learning Technique

Authors:

Norshakirah AzizAuthors Info & Claims

ICSCA '22: Proceedings of the 2022 11th International Conference on Software and Computer Applications

Pages 143 - 148

https://doi.org/10.1145/3524304.3524337

Published: 06 June 2022 Publication History

Abstract

Every country has its own federal government. Each federal government will have its own financial account which consist of revenue and expenditure. Focusing on the revenue, it has many sources that includes three main categories. They are tax revenue, non-tax revenue and non-revenue receipts. The revenue will then be used for operational and development purposes. Currently in Malaysia, the federal government revenue is only using forecasting. This can cause large forecasting error. Though it can be overcome using predictive analytics. Since there are many machine learning methods available, the appropriate methods can be identified to do the prediction. Based on previous research, feed forward neural network (FFNN), random forest and linear regression seems to be the most suitable. After conducting several experiments, it is found that FFNN achieved highest accuracy, followed by random forest. As for linear regression, it does not achieve good accuracy, thus it is considered as a not suitable method to be used on the federal government revenue dataset.

References

[1]

V. H. Trieu, “Getting value from Business Intelligence systems: A review and research agenda,” Decis. Support Syst., vol. 93, pp. 111–124, 2017.

Digital Library

[2]

K. Gowthami and M. R. P. Kumar, “Study on Business Intelligence Tools for Enterprise Dashboard Development,” Int. Res. J. Eng. Technol., vol. 4, no. 4, pp. 2987–2992, 2017, [Online]. Available: https://www.irjet.net/archives/V4/i4/IRJET-V4I4721.pdf.

[3]

L. Lai, “Loan Default Prediction with Machine Learning Techniques,” in 2020 International Conference on Computer Communication and Network Security, CCNS 2020, 2020, pp. 5–9.

[4]

Y. Niu, L. Ying, J. Yang, M. Bao, and C. B. Sivaparthipan, “Organizational business intelligence and decision making using big data analytics,” Inf. Process. Manag., vol. 58, no. 6, p. 102725, 2021.

Digital Library

[5]

H. Jihal, M. A. Talhaoui, A. Daif, and M. Azzouazi, “Predictive Analytics as A Service on Moroccan Tax Evasion,” Artic. Int. J. Eng. Technol., vol. 7, no. April, pp. 90–92, 2018, [Online]. Available: https://www.researchgate.net/publication/332353664.

[6]

E. Ahmed, “The role of big data analytics in Internet of Things,” Comput. Networks, vol. 129, pp. 459–471, 2017.

Digital Library

[7]

Ministry of Finance Malaysia, “Fiscal Outlook and Federal Government Revenue Estimates 2021,” 2020, [Online]. Available: https://www.treasury.gov.my/index.php/en/fiscal-economy/fiscal-outlook-2021.html.

[8]

Ministry of Finance Malaysia, “Estimates of Federal Government Revenue 2019,” 2019, [Online]. Available: https://www.mof.gov.my/arkib/hasil/h_main.html.

[9]

Ministry of Finance Malaysia, “Estimates of Federal Government Revenue 2020,” pp. 183–196, 2020, [Online]. Available: https://www.mof.gov.my/arkib/hasil/h_main.html.

[10]

L. Dadayan, “State Revenue Forecasts Before Covid-19 and Directions Forward,” pp. 1–9, 2020, [Online]. Available: https://www.urban.org/policy-centers/cross-center-initiatives/state-and-local-finance-.

[11]

H. Shahnazarian, M. Solberger, and E. Spånberg, “Forecasting and Analysing Corporate Tax Revenues in Sweden Using Bayesian VAR Models,” Finnish Econ. Pap., vol. 28, no. 1, pp. 50–74, 2017.

[12]

Chimilila and Cyril, “Forecasting Tax Revenue and its Volatility in Tanzania,” African J. Econ. Rev., vol. 5, no. 1, pp. 84–109, 2017.

[13]

B. Zhao, “Forecasting the New England States’ Tax Revenues in the Time of the COVID-19 Pandemic,” 2020, [Online]. Available: https://ssrn.com/abstract=3648649.

[14]

S. K. Babu and S. Vasavi, “Predictive analytics as a service on tax evasion using Gaussian Regression process,” Helix, vol. 7, no. 5, pp. 1988–1993, 2017.

[15]

S. B. Jang, “A Design of a Tax Prediction System based on Artificial Neural Network,” 2019 Int. Conf. Platf. Technol. Serv. PlatCon 2019 - Proc., pp. 1–4, 2019.

[16]

A. Kibekbaev and E. Duman, “Benchmarking regression algorithms for income prediction modeling,” Inf. Syst., vol. 61, pp. 40–52, 2016.

Digital Library

[17]

L. M. Chaitanya, D. H. Ravi, and R. Bharathi, “Fuel Price Prediction Using RNN,” 2018 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2018, pp. 1510–1514, 2018.

[18]

U. Inyaem, “Construction Model Using Machine Learning Techniques for the Prediction of Rice Produce for Farmers,” 2018 3rd IEEE Int. Conf. Image, Vis. Comput. ICIVC 2018, pp. 870–874, 2018.

[19]

S. Dutta, A. Halder, and K. Dasgupta, “Design of a novel prediction engine for predicting suitable salary for a job,” Proc. - 2018 4th IEEE Int. Conf. Res. Comput. Intell. Commun. Networks, ICRCICN 2018, pp. 275–279, 2018.

[20]

M. F. Mukhlishin, R. Saputra, and A. Wibowo, “Predicting house sale price using fuzzy logic, Artificial Neural Network and K-Nearest Neighbor,” Proc. - 2017 1st Int. Conf. Informatics Comput. Sci. ICICoS 2017, vol. 2018-Janua, pp. 171–176, 2017.

[21]

Syaharuddin, D. Pramita, T. Nusantara, and Subanji, “Accuracy analysis of ANN back propagation, neuro-fuzzy, and radial basis function: A case of HDI forecasting,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 1299–1304, 2019.

[22]

S. V. Chang, K. O. Chin, K. S. Gan, R. Alfred, and P. Anthony, “External constraints of neural cognition for CIMB stock closing price prediction,” Pertanika J. Sci. Technol., vol. 25, no. S6, pp. 29–38, 2017.

[23]

P. Khongchai and P. Songmuang, “Implement of salary prediction system to improve student motivation using data mining technique,” Proc. - 11th 2016 Int. Conf. Knowledge, Inf. Creat. Support Syst. KICSS 2016, pp. 0–5, 2016.

[24]

P. Khongchai and P. Songmuang, “Random Forest for Salary Prediction System to Improve Students’ Motivation,” Proc. - 12th Int. Conf. Signal Image Technol. Internet-Based Syst. SITIS 2016, pp. 637–642, 2016.

[25]

Y. Cai, Q. Yin, Q. Su, X. Huang, Y. Zhang, and T. Liu, “Prediction Method of Enterprise Return on Net Assets Based on Improved Random Forest Algorithm,” J. Phys. Conf. Ser., vol. 1682, no. 1, 2020.

[26]

W. Ticona, K. Figueiredo, and M. Vellasco, “Hybrid model based on genetic algorithms and neural networks to forecast tax collection: Application using endogenous and exogenous variables,” Proc. 2017 IEEE 24th Int. Congr. Electron. Electr. Eng. Comput. INTERCON 2017, pp. 5–8, 2017.

[27]

F. A. Lubis and Albarda, “Data Partition and Hidden Neuron Value Formulation Combination in Neural Network Prediction Model,” 2018.

[28]

J. Mathews, P. Mehta, S. Kuchibhotla, D. Bisht, S. B. Chintapalli, and S. V. K. Visweswara Rao, “Regression Analysis towards Estimating Tax Evasion in Goods and Services Tax,” Proc. - 2018 IEEE/WIC/ACM Int. Conf. Web Intell. WI 2018, pp. 758–761, 2019.

[29]

H. Höglund, “Tax payment default prediction using genetic algorithm-based variable selection,” Expert Syst. Appl., vol. 88, pp. 368–375, 2017.

Digital Library

[30]

C. P. López, M. J. D. Rodríguez, and S. de L. Santos, “Tax fraud detection through neural networks: An application using a sample of personal income taxpayers,” Futur. Internet, vol. 11, no. 4, 2019.

[31]

V. A. Ahnaf and Suharjito, “Financial revenue prediction model in mining industry using deep learning,” ICIC Express Lett., vol. 13, no. 11, pp. 987–993, 2019.

[32]

M. Milić, J. Milojković, I. Marković, and P. Nikolić, “Concurrent, performance-based methodology for increasing the accuracy and certainty of short-term neural prediction systems,” Comput. Intell. Neurosci., vol. 2019, 2019.

[33]

H. Razavi, H. Sarabadani, A. Karimisefat, and J. F. Lebraty, “Profitability Prediction for ATM Transactions Using Artificial Neural Networks: A Data-Driven Analysis,” 2019 IEEE 5th Conf. Knowl. Based Eng. Innov. KBEI 2019, pp. 661–665, 2019.

[34]

K. G. Nisha and K. Sreekumar, “A review and analysis of machine learning and statistical approaches for prediction,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2017, no. Icicct, pp. 135–139, 2017.

[35]

N. Darapaneni, S. Muthuraj, K. Prabakar, and M. Sridhar, “Demand and revenue forecasting through machine learning,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 328–331, 2019.

Cited By

Mendonça ASilva FCoutinho L(2024)Learning Model for Analytical Prediction of Tax Revenues from Tax Invoice InformationRevista Científica Multidisciplinar Núcleo do Conhecimento10.32749/nucleodoconhecimento.com.br/computer-engineering/learning-model(05-26)Online publication date: 10-Jul-2024
https://doi.org/10.32749/nucleodoconhecimento.com.br/computer-engineering/learning-model
Musa ZBalarabe Junaidu SIsmaeel Ahmad BKana AAbubakar Ibrahim A(2023)An Enhanced Predictive Analytics Model for Tax-Based OperationsInternational Journal on Perceptive and Cognitive Computing10.31436/ijpcc.v9i1.3439:1(44-49)Online publication date: 28-Jan-2023
https://doi.org/10.31436/ijpcc.v9i1.343

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ICSCA '22: Proceedings of the 2022 11th International Conference on Software and Computer Applications

February 2022

224 pages

ISBN:9781450385770

DOI:10.1145/3524304

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 June 2022

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Yayasan Universiti Teknologi Petronas (YUTP)

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ICSCA 2022

ICSCA 2022: 2022 11th International Conference on Software and Computer Applications

February 24 - 26, 2022

Melaka, Malaysia

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Mendonça ASilva FCoutinho L(2024)Learning Model for Analytical Prediction of Tax Revenues from Tax Invoice InformationRevista Científica Multidisciplinar Núcleo do Conhecimento10.32749/nucleodoconhecimento.com.br/computer-engineering/learning-model(05-26)Online publication date: 10-Jul-2024
https://doi.org/10.32749/nucleodoconhecimento.com.br/computer-engineering/learning-model
Musa ZBalarabe Junaidu SIsmaeel Ahmad BKana AAbubakar Ibrahim A(2023)An Enhanced Predictive Analytics Model for Tax-Based OperationsInternational Journal on Perceptive and Cognitive Computing10.31436/ijpcc.v9i1.3439:1(44-49)Online publication date: 28-Jan-2023
https://doi.org/10.31436/ijpcc.v9i1.343

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