Pengaruh Penggunaan Informasi Akuntansi Terhadap Keberhasilan Umkm Pada Kampung Yaba Maru
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh penggunaan informasi akuntansi terhadap keberhasilan UMKM pada Kampung Yaba Maru. Penelitian ini merupakan penelitian kuantitatif. Metode pengambilan sampel menggunakan teknik sampling Jenuh dengan sampel sebanyak 62. Teknik pengumpulan data dilakukan dengan kuesioner. Data penelitian menggunakan skala likert dengan bantuan program SPSS versi 26 dan metode analisis yang digunakan yaitu Analisis Regresi Linear Sederhana.Hasil penelitian ini menunjukkan bahwa Penggunaan informasi akuntansi secara efektif meningkatkan keberhasilan UMKM. Informasi ini membantu pengambilan keputusan, perencanaan, pengendalian, dan evaluasi keuangan, yang berkontribusi pada pengelolaan sumber daya yang lebih baik dan peningkatan profitabilitas. Studi menunjukkan bahwa 62,1% keberhasilan UMKM dipengaruhi oleh penggunaan informasi akuntansi. Oleh karena itu, sangat penting bagi UMKM untuk mengembangkan dan memanfaatkan informasi akuntansi secara optimal demi pertumbuhan dan kestabilan finansial
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".