MEMBANGUN SISTEM APLIKASI HUTANG PIUTANG PADA KOPERASI KARYAWAN MAKMUR NIAGA PT. WIKA BETON SUMUT, Tbk SEBAGAI SOLUSI MENGHINDARI KESALAHAN PENCATATAN
Bibliographic record
Abstract
The development of technology is currently growing rapidly, this requires every company to continue to improve the system used for the management of its business. Expected in each field using the computerized system. No exception in the field of accounts payable. In the Commercial Employees Cooperative Makmur Wika Beton Sumatra, tbk accounts payable data management is still manual and very likely there was an error in the recording. The number of tables of contents and folders different storage places is very influential on the data inputting error. Accounts payable so the data is very vulnerable to errors and data becomes invalid. The authors felt the need to discuss the management of data on goods production accounts payable so the future can be computerized. Each transaction is concerned with accounts payable starting from ordering goods, until payment of accounts payable will be processed and stored disistem. The system will be discussed in this thesis is a system that is able to manage every transaction accounts payable for goods production that occurred in the Commercial Employees Cooperative Makmur . With a computerized system admin will be able to manage data Kopkar payable as easily as searching the details of the data based on the number spb faster , the possibility of duplicate data can be solved , as well as the report clearly displays the classification of loans receivable based on the number spb.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.075 |
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".