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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia
Adi Marhadi
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Computer Science & IT Dentistry Economics, Econometrics & Finance Education Library & Information Science

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RPLUGIN.ECONOMETRICS: PAKET GRAPHICAL USER INTERFACE OPEN SOURCE UNTUK ANALISIS RUNTUN WAKTU MENGGUNAKAN PERANGKAT LUNAK R Rosadi, Dedi; Marhadi, Adi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 4, Juli 2009
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (279.502 KB) | DOI: 10.12962/j24068535.v7i4.a85

Abstract

R (R Development Core Team, 2009) is one of the open source software that is popular and has become "lingua franca" or standard language for the purposes of computing the current statistics. In this paper, will be introduced and discussed RcmdrPlugin.Econometrics package (Rosadi, Marhadi and Rahmatullah, 2009), which is a GUI version (Graphical User Interface) of R for the purposes of econometric analysis or time series. RcmdrPlugin.Econometrics package is an additional menu (plug-in) which provided for the R Commander, which is the most popular GUI of R. To illustrate the design philosophy of this package, provided also illustrate the usage of the RcmdrPlugin.Econometrics package for the exponential smoothing.
Assessing Taxpayers' Ability to Pay: A Machine Learning Approach Sukaryo; Marhadi, Adi
Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia Vol. 6 No. 2 (2025): April: Harnessing Data, Enhancing Compliance, and Empowering Policy
Publisher : Directorate General of Taxes

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52869/st.v6i2.530

Abstract

Tax revenue remains one of the challenging fiscal issues in Indonesia. Improving tax collection performance through comprehensive reform has been an influential agenda, especially for the Directorate General of Taxes. One of the critical improvement areas is the utilization of information technology in tax assessment and audit functions. This study explores the taxpayers’ ability concept as a complementary measure to the existing taxpayer monitoring module, particularly in case selection and targeting functions under the Compliance Risk Management (CRM) framework. The 5Cs of credit analysis (Character, Capacity, Capital, Condition, and Collateral) are employed as proxies for the taxpayers’ ability to pay. This research aims to identify the most effective machine learning algorithm for classifying taxpayers' ability to pay to enhance the CRM's effectiveness for corporate taxpayers, limited to those administered in large and medium tax offices. Several machine learning algorithms were tested, including logistic regression as a baseline comparison, based on the quantitative and qualitative performance comparison. The findings reveal that the Light Gradient Boosting Machine algorithm provides the most effective results in terms of both accuracy and computational efficiency. However, several challenges need to be addressed to improve the model implementation.
Co-Authors Dedi Rosadi Sukaryo