Sistem Pendukung Keputusan Pemberian Jumlah Pinjaman Kepada Calon Nasabah Bumdes Menggunakan Metode Topsis (Studi Kasus Bumdes Gergas Mandiri Kecamatan Wampu)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract

 
 
 
 The development of the savings and loan business is currently growing rapidly as a financial institution in alleviating poverty . BumDes is a business owned by a village or sub-district that is engaged in lending or channeling funds to people who need to develop their business. The BUMDes conducts deliberation meetings in determining loan granting. There is often disagreement between the parties that will borrow. This resulted in unequal distribution of loans to BUMDes members. Although the determination of the granting of the loan amount is fully determined by the BUMDes However, this Decision Support System will display the highest to lowest priorities of the prospective customer , so that it will facilitate and assist the BUMDes in making decisions. TOPSIS uses the principle that the chosen alternative must have the closest distance from the positive ideal solution and the longest distance (farthest) from the negative ideal solution to determine the relative proximity of an alternative to the optimal solution.
 
 
 
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it