ANALISIS TINGKAT PENGEMBALIAN INVESTASI PT. CIPUTRA DEVELOPMENT TBK SEBELUM DAN SESUDAH PENGUMUMAN PANDEMI COVID-19
Bibliographic record
Abstract
This study aims to determine the rate of return on investment at PT. CiputraDevelopment Tbk before and after the announcement of the covid-19 pandemic bycomparing the Return On Investment Ratio with The Industry Average Ratio. Theanalytical tool used in this study is the profitability ratio, namely the Return OnInvestment (ROI) Ratio and using The Industry Average Ratio. Data collectiontechniques are carried out by means of literature research, by collecting secondarydata in the form of financial reports of PT. Ciputra Development Tbk andcompanies in the property industry sector which are used as the calculation of theindustry average ratio. The analytical tool used in this study is the profitabilityratio, namely the Return On Investment (ROI) Ratio and using The IndustryAverage Ratio. The results of this study is the rate of return on investment at PT.Ciputra Development Tbk in 2019 the first quarter of the company was in goodcondition, in 2019 the second quarter of the company was in poor condition, whilein 2019 the third quarter to 2020 the third quarter of the company was back in goodcondition. The conclusion of this study is that the condition of the company is notgood in 2019 the second quarter, because the return on investment ratio is belowthe industry average ratio.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".