Analisis Tingkat Perputaran Piutang pada PT.PLN (Persero) Unit Layanan Pelanggan (ULP) Lubuk Alung Tahun 2019
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
Manage recei vables gi ven, which can be seen from the ratio of receivables activity where this ratio meas ures the effectiveness of the company in collecting recei vables. The recei vable activity ratio consists of recei vables turnover and t he average peri od of collection of recei vables. This type of research is a simple applied research with quantitative descriptive analysis technique that describes the level of receivables turnover and the effectiveness of collecting receivables PT.PLN (Persero) Lubuk Alung Customer Service Unit. The data in this study uses secondary data in the form of an overview report of the balance of receivables obtained indirectly from the object of research. Based on the results of the study, it was found that in 2019, the receivable turnover rate of PT PLN (Persero) Lubuk Alung Customer Service Unit in the first quarter of the receivables turnover was 4 times and the receivables collection period was 10 days, the second quarter the receivables turnover was 3 times and the collection period was 10 days. 12 days, 3rd quarter of receivables collection and collection period of 13 days, 4th quarter of receivables collection 3 times and collection period of 11 days. The effectiveness of the receivables turnover of PT PLN (Persero) Lubuk Alung Customer Service Unit in collecting receivables is quite good because it is still under the conditions determined by the company between 10-15 days.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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