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Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia
Published by Direktorat Jenderal Pajak
ISSN : -     EISSN : 26865718     DOI : https://doi.org/10.52869/st.v2i2
Core Subject : Economy,
Economics, Econometrics & Finance
Scientax: Jurnal Kajian Ilmiah Perpajakan Indonesia, merupakan jurnal ilmiah perpajakan yang diterbitkan oleh Direktorat Jenderal Pajak yang memuat hasil penelitian ilmiah berupa kajian, baik secara teori maupun empiris, atas isu dan problematika seputar perpajakan. Setiap artikel yang diterbitkan di Scientax merupakan artikel hasil kajian dan riset yang bersumber dari studi literatur, review literatur, penelitian lapangan, best practice dan/atau kombinasi dari setiap kegiatan ilmiah tersebut. Artikel yang diterbitkan dalam Scientax telah melalui proses review, evaluasi dan penyuntingan oleh Dewan Redaksi, Mitra Bestari dan Anggota Staf Editorial. Scientax diterbitkan 2 (dua) kali dalam setahun, yaitu Oktober dan April, dan terbuka untuk umum, praktisi, peneliti, pegawai, dan pemerhati masalah perpajakan.
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Keuangan
Articles 7 Documents
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Search results for , issue "Vol. 6 No. 2 (2025): April: Harnessing Data, Enhancing Compliance, and Empowering Policy" : 7 Documents clear
A Review of Taxation Aspect of Cash Poolings Based on Indonesian Regulations Yanurwenda, Benny Oktis; Suryawati, Rindah Febriana
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.v6i2.529

Abstract

Cash poolings are typically formed by companies in a single business group. Therefore, most of the transactions are affiliated transactions that must meet the Arm’s Length Principles. This study reviews the implementation of the Arm’s Length Principles in cash poolings. This research utilized a qualitative approach by reviewing elements of cash pooling transactions with Indonesia’s regulations and best practices in transfer pricing. The study concluded that implementing transfer pricing regulations in cash pooling arrangements would depend on the role of the leader. Based on the role of the leader, affiliated transactions in cash pooling are payment of loan interest and payment of interest on deposits in the scheme of the leader as an in-house bank and payment of fees in the scheme of the leader as a service provider. The transactions then need to be examined for compliance with the Arm’s Length Principles, including evaluating price indicators and selecting the tested party of each transaction.
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.
Data Mining to Detect Fraud Patterns in a Taxpayer’s Financial Statement Ginanjar, Achmad; Wibowo, Agung Septia
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.v6i2.571

Abstract

The application of machine learning in the analysis of financial statements is a relatively underexplored area compared to mainstream data mining fields, such as natural language processing (NLP) and image analysis, yet it holds significant potential. This study investigates the use of advanced linear regression techniques to identify patterns in taxpayers’ financial statements, employing a conceptual approach that combines both vertical and horizontal financial statement analysis methods. Using financial statement data reported to the Indonesian Tax Administration and historical taxation audit records,this study determines the presence of identifiable patterns. This study applies linear regression to financial statement account values to measure changes over the years and uses yearly account values to create unique data points representing each entity. A clustering method is then employed to group entities with similar patterns. The findings indicate that the proposed method can effectively analyse how entities report their financial statements over time and cluster them based on the likelihood of committing fraud, as inferred from historical audit records. These patterns are validated by instances of underpayment or overpayment of corporate income taxes identified during tax audits. By examining the clustering results, the study reveals that certain clusters accurately align with labelled patterns, correctly identifying 2 of 3 labels. The comparison between unsupervised clustering and labelled criteria demonstrates a significant fitness probability.
Dealing with Last-Mile Analytics: Evidence from Indonesian Tax Administration through Practice Research Darono, Agung
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.v6i2.592

Abstract

This study employs a “practice-as-research” approach to investigate the role of analytics as a pivotal component of Indonesia’s tax administration system, specifically focusing on addressing tax gaps. Tax analytics systems and applications operate centrally to generate data that support several tax administration functions, including taxpayer registration, compliance monitoring, dispute resolution, and law enforcement. However, the tax officers—who serve as end-users of this data—frequently encounter the “last-mile problem”, where the data provided is not immediately actionable. Consequently, such tax officers are often required to develop their own data pipelines to further process and analyze the data before it can be effectively used for decision-making. This study identifies two categories of last-mile issues: those that can be eliminated and those that can only be mitigated to a limited extent. Two key recommendations are proposed to address these challenges. First, existing analytics applications should enhance taxpayer profile data by integrating the most comprehensive analytics outcomes, including compliance risk profiling. This can be achieved by implementing a “reverse-ETL” approach to improve existing analytics applications, facilitating the seamless flow of processed data back into operational system data. Second, the study advocates for more flexible self-service analytics platform for scenarios where last-mile challenges are unavoidable. This could be an analytics sandbox or a data-as-a-product approach that leverages containerization to enable tax officers to process and analyze data independently. These recommendations aim to improve the efficiency and effectiveness of Indonesia’s tax administration system by addressing the critical last-mile challenges faced by tax officers, thereby enhancing the overall utility of analytics in supporting tax-related decision-making processes.
Evaluasi Kebijakan Fasilitas Pajak untuk Mendorong Riset dan Inovasi di Indonesia Bhuana, Bhima Chandra; Ardiansyah, Benny Gunawan
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.v6i2.892

Abstract

The government of the Republic of Indonesia has implemented tax facilities to encourage businesses and industries to conduct research and development to optimize Indonesia's economic growth. Still, taxpayers have not widely benefited from these facilities. This study aims to explore the relevance of the tax facility policy to research and innovation activities in Indonesia and evaluate the implementation problems. Grounded on Sabatier and Mazmanian's public policy implementation framework, data were collected and analyzed using a qualitative research method, through a series of interviews with policy actors. The study found that: (1) the tax facility policy has minor relevance to the encouragement of research and innovation activities in Indonesia due to the problem of the unestablished science and technology ecosystem (2) the regulatory aspect of PMK Number 153/PMK.010/2020 has not provided adequate support for implementing ministries/agencies in addressing the socio-economic challenges of Indonesian society, as well the negative attitudes and perceptions of businesses and industries towards the policy. The findings are expected to stimulate further discussion on research and innovation in Indonesia to complete the factual basis that the government can use in determining future policy directions.
Interest Limitation Rules and Corporate Tax Avoidance: A Cross-Country Analysis Dewantara, Andreas Rossi
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.v6i2.904

Abstract

This study examines the impact of interest limitation rules on corporate tax avoidance and financing decisions. Interest from debts is tax-deductible, making debt financing attractive for firms, yet it also poses a risk for tax avoidance, leading to global tax revenue losses estimated between $125 to $280 billion annually. Tax authorities have implemented rules limiting interest deductibility, such as the debt-to-equity ratio ("thin-capitalization rule") and the interest-to-EBITDA ratio ("earnings stripping rule"), to curb this. Using a novel regression discontinuity design and panel data from 33 countries, the study finds no strong evidence that these rules significantly deter tax avoidance. However, it suggests the thin-capitalization rule might be marginally more effective than the earnings stripping rule. Our study proposes that adjusting the debt-to-equity ratio threshold to 2:1 could yield better outcomes in reducing tax avoidance for countries with a thin-capitalization rule. For countries with an earnings stripping rule, a stronger enforcement is recommended. The study encourages future research to explore the interaction with other tax regulations, such as the de minimis rule and arm’s length principles.
Capabilities of Data Quality Assurance Section and Performance of Unit in the Directorate General of Taxes: The Moderating Role of Data Quality Utami, Nugraheni Dwi; Arifin, Imam; Tatariyanto, Firman
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.v6i2.913

Abstract

Identifying data quality drivers is increasingly crucial for DGT because it helps achieve goals, maintain taxpayer compliance, increase efficiency, and improve business process accuracy. This study aims to identify whether strategic resources within an organisation influence its ability to achieve sustainable performance. Using the resource-based view theory, this study tests a moderated mediation model to examine the role of data quality on the relationship between the capabilities of the PKD Section at the tax office and the performance of that office at various locations in Indonesia. The proposed research model was validated using online surveys and structural equation modeling. The results indicate that the capabilities of the PKD Section significantly contribute to performance improvement through a path other than data. However, there is still a need to improve data quality within an organisation. Most notably, the findings demonstrate that the PKD Section's capabilities correlate with improving data quality management; however, users have not utilized the data produced by the PKD Section, nor have they supported analysis to enhance performance. Our findings confirmed that achieving data quality requires more than a technical standpoint. Instead, it must be viewed within a broader organisational framework encompassing management, resources, and culture. This research expanded the theoretical scope of capabilities and performance by introducing the concept of data quality management, which includes aspects such as planning, monitoring, assurance, and improvement. Additionally, we incorporated data quality aspects, such as data accuracy, timeliness, completeness, consistency, uniqueness, and validity, that meet the requirements of data users.

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