PENGARUH INEFFECTIVE MONITORING, FINANCIAL STABILITY, DAN CORPORATE GOVERNANCE, TERHADAP FINANCIAL STATEMENT FRAUD
Bibliographic record
Abstract
Penelitian ini memiliki maksud dalam memberikan pembuktian terhadap pengaruh apa yang diberikan Ineffective Monitoring (X1), Financial Stability (X2), dan Corporate Governance (X3), terhadap Financial Statement Fraud pada perusahaan Sektor Industri Barang Konsumsi yang terdaftar di Bursa Efek Indonesia tahun 2018-2021. Beneish M-Score Model dilakukan penggunaannya dalam pengukuran kecurangan laporan keuangan. Menggunakan data sekunder dalam memperoleh data melalui laporan keuangan yang telah diaudit yang pemerolehannya pada website BEI yaitu www.idx.co.id. Penelitian ini menerapkan purposive sampling dalam pengumpulan sampel data yang berjumlah 132 perusahaan, dan menggunakan analisis regresi logistik dalam menguji hubungan variabel-variabel penelitian. Berdasarkan pengujian yang telah dilakukan, ditemukan bahwa Financial Statement Fraud tidak dipengaruhi signifikan oleh Ineffective Monitoring, Financial Statement Fraud dapat dipengaruhi signifikan oleh Financial Stability, Financial Statement Fraud dapat dipengaruhi signifikan oleh Corporate Governance. Secara simultan variabel bebas memiliki pengaruh berupa signifikan pada Financial Statement Fraud.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".