Analysis of Zakat Accounting and the Role of the Internal Control System based on Financial Accounting Standards Guidelines (PSAK 109) Zakat Agency in Indonesia
Bibliographic record
Abstract
This research was carried out because of the phenomenon of the large potential of zakat in Indonesia and the large number of zakat funds that were corrupted due to unaccountable financial reporting. Many payers of zakat (muzakki) still do not believe in National Zakat Agency (BAZNAS), so zakat payments are often not made through the official of BAZNAS. This research was carried out through a survey which was designed to examine the accountability of financial reporting at the amil zakat and amil zakat institutions in Jakarta and Banten, related to internal control competencies and financial reporting accountability. The expected results of this study are an increase in the accountability of financial reporting by conducting sharia accounting training for staff in amil zakat and amil zakat institutions in Jakarta and Banten. The goal of this study is to contribute scientifically to the science of sharia accounting, specifically accounting for zakat, and to assist accounting departments in preparing financial statements. The results of this study state that compliance with the application of zakat accounting with Financial Accounting Standards Guidelines (PSAK 109) had no significant effect on financial reporting accountability, while the role of the internal control system had a significant positive effect on financial reporting accountability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".