SISTEM APLIKASI PERSEDIAAN BARANG JADI MENGGUNAKAN METODE FIFO PADA PT.PRIMA INDAH UNTUK MENGHINDARI REDUNDANSI LAPORAN PERSEDIAAN
Bibliographic record
Abstract
In the rapid technological developments and rapidly prosecute any businesses or other institutions to further improve any systems used in management. Expected every facet of business activity using a computerized system . Likewise with existing technology PT. Prima Indah. PT. Prima Indah in the calculation of finished goods inventory still using manual systems and cause problems in the form of ever making the report and the absence of a special database that stores data inventory . So it needs a special system that handles the calculation of finished goods inventories in order to facilitate the knowing of finished goods inventory in the warehouse . In that regard there are some things that should be discussed about how the calculation of finished goods inventory at PT. Prima Indah and how the entrance and exit of preparing reports and statements of finished goods inventory accumulation . The final task is to try to discuss and analyze problems that occur in the calculation of the inventory of finished goods . And the result will be directed to the company as a suggestion to use a computerized system in order to save costs and time as well as the accuracy of the information produced .
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.023 |
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".