Faktor-Faktor yang Memengaruhi Implementasi SAK di UMKM Gudeg Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
This study aims to determine the effect of education level, understanding of accounting, information technology, and business size on the implementation of SAK on MSMEs Gudeg in the Special Region of Yogyakarta.The population in this study is the Gudeg MSME actors in the Special Region of Yogyakarta who are registered in the GrabFood application, totaling 88 business actors.Sampling by purposive sampling method was carried out through several criteria, in order to obtain 45 targeted MSMEs.The data analysis technique is through multiple linear regression analysis.Of the 4 hypotheses proposed, there are 2 accepted hypotheses and 2 rejected hypotheses.The accepted hypotheses are H2 and H3, namely accounting understanding has a positive effect on the implementation of SAK on MSMEs and information technology has a positive effect on SAK implementation on MSMEs.While the rejected hypotheses are H1 and H4, with the result that the level of education has no effect on the implementation of SAK on MSMEs and business size does not affect the implementation of SAK on MSMEs.The results of this research show that along with the existing developments, access to compiling financial reports is easier due to an understanding of accounting and information technology which no longer depends on the level of education and the size of the business.This research is expected to be a reference for MSME actors as well as policy makers so that effectiveness in efforts to implement SAK on MSMEs can be carried out, mainly by increasing accounting understanding and the use of information technology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".