IS THERE ANY PERSONAL DATA PROTECTION IN THE CORE TAX ADMINISTRATION SYSTEM?
Bibliographic record
Abstract
Objective: This study examines the adequacy of personal data protection within Indonesia’s Core Tax Administration System (CTAS), focusing on compliance with the national Personal Data Protection Law and alignment with international standards. Theoretical Framework: The research is grounded in legal and regulatory analysis, referencing Indonesia’s Personal Data Protection Law (UU PDP) and international frameworks such as the EU’s GDPR and Canada’s PIPEDA, to evaluate the protection of taxpayer data in digital tax administration systems Method: A qualitative approach is utilized, involving literature review, document analysis of relevant laws and policies, and comparative analysis with data protection practices in other jurisdictions. The study synthesizes findings from academic sources, legal documents, and international case studies Results and Discussion: The findings reveal significant gaps in the implementation of personal data protection in CTAS. Despite the enactment of the PDP Law, Indonesia lacks specific regulations and enforcement mechanisms tailored to the tax sector, leaving sensitive taxpayer data vulnerable to unauthorized access, misuse, and breaches. Comparative analysis highlights that international best practices require clear guidelines, robust security protocols (such as encryption and access controls), regular audits, and a culture of transparency and accountability. The absence of a dedicated data protection authority and insufficient employee training further exacerbate risks Research Implications: The study underscores the urgent need for Indonesia to strengthen its legal and operational framework for data protection in tax administration. Recommendations include developing sector-specific regulations, enhancing technological safeguards, instituting regular audits, and fostering public awareness to ensure taxpayer trust and system integrity Originality/Value: This research provides a comprehensive, context-specific analysis of personal data protection challenges in Indonesia’s CTAS, offering actionable recommendations informed by international standards and highlighting the critical importance of privacy in digital tax systems
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".