Sains Malaysiana 50(7)(2021): 2059-2077


Digital Economy Tax Compliance Model in Malaysia using Machine Learning Approach

(Model Pematuhan Cukai Ekonomi Digital di Malaysia menggunakan Pendekatan Pembelajaran Mesin)




1Sub Section of Strategic Planning, Strategic Management and Information ICT, Department of Information Technology, Inland Revenue Board of Malaysia, 63000 Cyberjaya, Selangor Darul Ehsan, Malaysia


2Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia


Diserahkan: 10 Jun 2020/Diterima: 19 November 2020



The field of digital economy income tax compliance is still in its infancy. The limited collection of government income taxes has forced the Inland Revenue Board of Malaysia (IRBM) to develop a solution to improve the tax compliance of the digital economy sector so that its taxpayers may report voluntary income or take firm action. The ability to diagnose the taxpayer's compliance will ensure the IRBM effectively collects the income tax and gives revenues to the country. However, it gives challenges in extracting necessary knowledge from a large amount of data, leading to the need for a predictive model to detect the taxpayers' compliance level. This paper proposes the descriptive and predictive analytics models for predicting the digital economic income tax compliance in Malaysia. We conduct descriptive analytics to explore and extract a summary of data for initial understanding. Through a brief description of the descriptive model, the data distribution in