Pengembangan Metode Pemulihan Penerimaan Pajak Pasca Pandemi Covid-19
Bibliographic record
Abstract
The case of the Covid-19 pandemic caused a slowdown in economic growth in Indonesia, which in turn resulted in a decrease in the amount of tax revenue, which eventually led the government to provide tax incentives to taxpayers. The COVID-19 pandemic has had a tremendous impact on all countries in the world, including Indonesia. The Central Statistics Agency (BPS) announced that the Indonesian economy experienced a slowdown in the first quarter of 2020, which was 2.97%. Compared to the fourth quarter of 2019, Indonesia's economic growth was recorded at minus 2.41%. This study aims to develop a method of tax revenue that has been adapted to the conditions of the Covid-19 pandemic. This study uses an exploratory method with a quantitative approach. The data used are the annual reports of companies listed on the Indonesia Stock Exchange (IDX) in 2018 and 2019. The sample selection method uses a purposive sampling method and obtained 233 samples for 2018 and 227 samples for 2019. With the results in the form of suggestions given in improving Post-covid-19 tax revenues, namely (1) Optimization of the withholding tax (WHT) mechanism, (2) Imposition of Final PPh for non-SME taxpayers, and (3) Efficiency of tax collection costs. This proposal requires in-depth study before implementation
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".