FAKTOR-FAKTOR YANG MEMPENGARUHI PAJAK PERTAMBAHAN NILAI PADA PT. UNILEVER INDONESIA, TBK
Bibliographic record
Abstract
This research aims to determine the factors that can affect the value added tax such as inflation, interest rates, and the value of exports at PT. Unilever Indonesia, Tbk. Research with a quantitative approach with secondary data types, namely periodic data (time series). The population of this research is the company's financial statements PT. Unilever Indonesia, Tbk for the period of the quarter or quarter in the period 2006-2020 or 15 years with a sample of 60 data. Furthermore, the data were analyzed using multiple linear regression assisted by SPSS Version 26 program. The results showed that interest rates and export values had no effect on value added tax, while inflation had a significant effect on value added tax. Even though inflation can increase VAT through selling prices, but inflation that is too high can reduce people's purchasing power so that company sales will decrease, thus this can indicate to PT Unilever Indonesia, Tbk and also other companies that there is a need for detailed supervision of the supply chain. When inflation occurs, the company is advised to continue to produce in the amount it should and the sales chain such as distributors and retailers to continue to distribute normally, this aims to avoid product shortages and continue to increase inflation. In addition, the government is required to manage inflation effectively and efficiently so that the production strategy is not disrupted by price increases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.066 |
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".