Pengaruh Perputaran Kas, Perputaran Piutang, Perputaran Persediaan, dan Pertumbuhan Hutang terhadap Profitabilitas pada Perusahaan Sub Sektor Makanan dan Minuman yang terdaftar Di Bursa Efek Indonesia
Bibliographic record
Abstract
Penelitian ini dilakukan bertujuan untuk : (1) menganalisis pengaruh perputaran kas terhadap profitabilitas; (2) menganalisis pengaruh perputaran piutang terhadap profitabilitas; (3) menganalisis pengaruh perputaran persediaan terhadap profitabilitas; (4) menganalisis pengaruh pertumbuhan hutang terhadap profitabilitas; (5) menganalisis pengaruh perputaran kas, perputaran piutang, perputaran persediaan dan pertumbuhan hutang terhadap profitabilitas secara simultan. Penelitian ini menggunakan data sekunder. Teknik analisis data yang digunakan adalah uji asumsi klasik dan uji regresi linear berganda dengan menggunakan bantuan Statistical Product and Service Solution (SPSS). Hasil penelitian menunjukkan bahwa : (1) perputaran kas memiliki pengaruh negatif dan tidak signifikan terhadap profitabilitas; (2) perputaran piutang memiliki pengaruh negatif dan signifikan terhadap profitabilitas; (3) perputaran persediaan memiliki pengaruh positif dan signifikan terhadap profitabilitas; (4) pertumbuhan hutang memiliki pengaruh negatif dan tidak signifikan terhadap profitabilitas; (5) perputaran kas, perputaran piutang, perputaran persediaan, dan pertumbuhan hutang secara simultan memiliki pengaruh yang signifikan terhadap profitabilitas.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".