ANALISIS VARIABEL YANG MEMPENGARUHI RETURN ON EQUITY (ROE) (StudiPada Perusahaan Farmasi yang Listing di Bursa Efek Indonesia)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui seberapa besar kontribusi tingkat Current Ratio,Debt to Equity Ratio, Total Asset Turnover, Net Profit Margin terhadap besarnya Return on Equity (ROE) baik secara simultan maupun secara parsial. Selain itu tujuan yang kedua adalah untuk mengetahui variabel manakah yang dominan pengaruh nya terhadap ROE. Penelitian ini menggunakan sampel sebanyak 8 perusahaan farmasi yang terdaftar di Bursa Efek Indonesia (BEI) dengan aspek yang diteliti yaitu laporan keuangan tahunan periode 2009-2011. Sampel dalam penelitian diambil dengan purposive sampling . Uji hipotesis menggunakan uji F hitung dan t hitung. Untuk menganalisis data yang digunakan uji regresi berganda dan uji asumsi klasik yaitu uji normalitas, uji multikolinearitas, uji heteroskedastistas dan uji autokorelasi. Hasil penelitian menunjukkan ada pengaruh signifikan antara Current Ratio terhadap Return On Equity . Sedangakan variabel - variabel lain yaitu Debt to Equity Ratio , Total Asset Turnover , dan Net Profit Margin tidak memiliki pengaruh yang signifikan terhadap Return On Equity. Kata Kunci :Perusahaan Farmasi, Current Ratio,Debt to Equity Ratio, Total Asset Turnover, Net Profit Margin , Return On Equity
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".