IPTEKS DIGITALIZATION TREASURY PADA KANTOR WILAYAH DIREKTORAT JENDERAL PERBENDAHARAAN PROVINSI SULAWESI UTARA
Bibliographic record
Abstract
Management of state finances is certainly inseparable from the development of the era. The more advanced technology is, the easier it should be for management of state finances. Therefore, it is necessary to implement a system that has been digitized so the benefits of this rapid technological development can be felt by all parties. This is what underlies changes in the finance ministry of the Republic of Indonesian especially in the Directorate General of Treasury. The state treasury system and state budget (STSSB) and agency-level financial application system (AFAS) that support file input and adjustment processes in the Directorate General of Treasury and work units are part of digitalization that has been carried out at the Director General of Treasury., so that all facilities offered by technology in this era can lead to superior treasury manager at the world level. Development and improvement not only stop at the system, but also on the human resources that run the system. The quality of human resources should be improved.Keywords : Digitalization, Information, Human Resources, AFAS, and STSSB
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".