INCOME SMOOTHING DAN FAKTOR-FAKTOR YANG MEMPENGARUHINYA PADA PERUSAHAAN PARIWISATA YANG TERDAFTAR DI BURSA EFEK INDONESIA
Bibliographic record
Abstract
Penelitian ini dilakukan dengan tujuan untuk mengetahui pengaruh Profitabilitas, likuiditas, financial leverage dan struktur kepemilikan terhadap income smoothing pada Perusahaan Pariwisata yang Terdaftar di Bursa Efek Indonesia. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan asosiatif. Teknik pengumpulan data dalam penelitian ini menggunakan teknik dokumentasi. Jumlah populasi sebanyak 23 perusahaan tetapi hanya terdapat 11 perusahaan dengan 6 tahun Pengamatan sehingga jumlah sample 66 sampel. Analisis data menggunakan Analisis regresi linier berganda.Pengolahan data dalam penelitian ini menggunakan program software SPSS (Statistic Package for the Social Sciens) versi 22.00 Hasil penelitian menunjukkan bahwa secara parsial Profitabilitas berpengaruh positif dan signifikan terhadap income smoothing, sedangkan likuiditas, financial leverage dan struktur kepemilikan berpengaruh negatif dan tidak signifikan terhadap income smoothing. Secara simultan Profitabilitas, likuiditas, financial leverage dan struktur kepemilikan berpengaruh dan signifikan terhadap income smoothing pada Perususahaan Pariwisata yang terdaftar di Bursa Efek Indonesia periode 2012-2017.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".