Analysis of Cost of Goods Sold Before and After The Issuance of Regulation of the Minister of Finance (PMK) No. 97 / PMK.010 / 2015 In The Metal Industry of Iron and Steel Listed on BEI
Bibliographic record
Abstract
The external factor affecting the basic iron and steel industry in Indonesia is the Regulation of the Minister of Finance of Indonesia concerning the imposition of import duty tariff on imported goods. The purpose of this study is: to determine the difference in cost of goods sold before and after the Minister of Finance Regulation no. 97 / PMK.010 / 2015. This research uses descriptive research type with quantitative, which measured by using comparative method / comparison with SPSS 23,00. The population of this research is sub metal company and the like listed on Indonesia Stock Exchange (IDX) from 3rd Quarter 2014 to 1st Quarter 2016. Samples are determined purpuse sampling method, with the sample number is 11 companies. The data used in this research is secondary data. Technique of collecting data using documentation method through IDX official website: www.idx.co.id. hypothesis testing using Wilcoxon Signed Ranks Test. It is known that the result of the significance level of 0.768> 0.05.So the conclusion is: there is no significant difference in cost of goods sold before and after the Regulation of the Minister of Finance No. 97 / PMK.010 / 2015.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".