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Record W4409907887 · doi:10.55908/sdgs.v13i4.4396

IS THERE ANY PERSONAL DATA PROTECTION IN THE CORE TAX ADMINISTRATION SYSTEM?

2025· article· en· W4409907887 on OpenAlexaboutno aff
Loso Judijanto

Bibliographic record

VenueJournal of Law and Sustainable Development · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAdministration (probate law)Core (optical fiber)BusinessTax administrationComputer securityComputer scienceEconomicsPolitical sciencePublic economicsTelecommunicationsTax reformLaw

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.269
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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