PENERAPAN SISTEM INFORMASI MANAJEMEN ANALISIS KELEMBAGAAN KABUPATEN CIANJUR DALAM MENINGKATKAN KINERJA BKPSDM KABUPATEN CIANJUR
Bibliographic record
Abstract
This research aims to analyze the application of the Cianjur Regency Institutional Analysis Management Information System (Sianjab Manjur) in improving the performance of the Cianjur Regency BKPSDM, with a focus on the implementation of Sianjab Manjur as well as the supporting factors and obstacles faced and the efforts made by the Cianjur Regency BKPSDM in overcoming obstacles. the. The research methods used include interviews, secondary data collection, and data analysis. The research results show that although Sianjab Manjur has had a positive impact in improving the performance of BKPSDM Cianjur Regency, there are several challenges that need to be faced in its implementation. The first challenge is related to data security. As an information system that stores sensitive data regarding personnel, Sianjab Manjur must be able to maintain data security properly so that it is not misused by unauthorized parties. The second challenge is related to employee acceptance and adaptation to new information systems. Using Sianjab Manjur requires acceptance and adaptation from employees, especially those who are used to manual systems. BKPSDM needs to increase outreach and training efforts to employees so that they can master and use Sianjab Manjur well.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".