Implementasi Panel Data Analysis untuk Seleksi Fitur Capital Structure Sebelum dan Saat Pelaksanaan Pembangunan Infrastruktur pada Perusahaan Indonesia yang Terdaftar di BEI
Bibliographic record
Abstract
Rencana pembangunan infrastruktur dipercaya menjadi penyebab meningkatnya rasio utang BUMN terhadap modal. Penelitian ini bertujuan untuk membuktikan apakah ada perbedaan leverage BUMN yang signifikan sebelum (2010-2014) dan saat pelaksanaan (2015-2019) rencana pembangunan infrastruktur Presiden Jokowi. Populasi penelitian adalah perusahaan non-finansial yang terdaftar di BEI pada periode 2010-2019. Data dianalisa dengan paired t-test dan panel data regression untuk mengetahui pengaruh state ownership, firm size, profitability, growth, tangibility, liquidity, firm risk, and debt tax shield pada capital structure. Hasil penelitian menunjukkan bahwa ada perbedaan signifikan antara tingkat leverage BUMN periode 2010-2014 dan 2015-2019. Leverage dan profitability merupakan faktor penentu yang signifikan di dua periode, sementara state ownership tidak signifikan di dua periode tersebut. Growth dan debt tax shield merupakah faktor yang signifikan di periode 2010-2014, tapi tidak di periode 2015-2019. Di sisi lain, tangibility tidak memiliki pengaruh signifikan di periode 2010-2014, tapi memiliki pengaruh signifikan di periode 2015-2019.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".