ANALISIS PERUBAHAN TARIF PROGRESIF PAJAK PENGHASILAN ORANG PRIBADI DALAM UNDANG-UNDANG HARMONISASI PERATURAN PERPAJAKAN DITINJAU DARI AZAS KEADILAN
Bibliographic record
Abstract
The Progressive Tax Rates that apply to Individual Taxpayers in Indonesia have undergone several changes, the most recent being the enactment of the Tax Regulations Harmonization Law (UU HPP) which will take effect from the 2022 Fiscal Year. can better realize the principle of justice and improve taxpayer compliance. The purpose of this study is to describe the impact of progressive rate changes in the calculation of Income Tax Article 21 and to analyze how the principle of justice is applied in the progressive tax rate changes. The research methodology used in this research is descriptive analysis, collecting data obtained from documentation studies and case studies. The results of the discussion show that the progressive rate change for Individual Taxpayers whose total taxable income does not exceed Rp. 5 billion a year causes the amount of tax payable in the calculation of Income Tax Article 21 to be smaller. The impact of the change in tariff has not been seen significantly because data on the class of taxpayers who are subject to the highest tariff has not been found. The progressive rate change in the HPP Law is intended to increase the progressiveness in the imposition of Personal Income Tax by adding a new top layer with a higher income range with the aim of further realizing the principles of justice and partiality to low-income taxpayers
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".