PEMETAAN DAN MODEL KEBUTUHAN LITERASI KEUANGANG UMKM DI KECAMATAN TEMBALANG
Bibliographic record
Abstract
UMKM are really need financial literacy in order to be able to manage and make wise business financial policy decisions. Central Java's financial literacy index ranges from conventional to sharia 47.58 percent, 11.78 percent. This study aims to find out how to map and model financial literacy in UMKM in Tembalang District. This research includes all UMKM in Tembalang District. By using non-probability purposive random sampling of customers. Respondents are UMKM actors who had KTPsin the Tembalang District. In this study, a sample of 97 UMKM respondents was taken in Tembalang District. Data analysis in this study was carried out descriptively using SPSS software. focuses on mapping financial information to the community and the need for financial literacy, especially UMKM in Tembalang District. The highest level of financial literacy is in Banking with a score of 44, and the lowest in Islamic finance is 10. The five financial literacy needs of the financial industry and banks for UMKM consist of Savings, KUR, UMi Financing, Profit Sharing Financing and Credit. Others Cash Flow and Income Statement
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".