IMPLEMENTASI E-KINERJA DALAM MENINGKATKAN PRODUKTIVITAS KERJA DI BAPPEDA KABUPATEN NAGAN RAYA
Bibliographic record
Abstract
E-Kinerja is an electronic system arising from the accelerated development of increasingly sophisticated communication and technology. This study aims to identify, analyze and assess the application of new technology as an effort to increase the effectiveness and productivity of PNS/ASN performance in work activities through the use of e-performance. This research is a type of qualitative research with a case study method presented with descriptive data in the form of interviews from informants, field observations and studies of documents related to objects. The results showed that the implementation of E-Kinerja to increase the productivity of civil servants' performance in BAPPEDA Nagan Raya Regency was not yet effective in increasing the productivity of employee performance, but only to increase the level of discipline on time with the level of adaptation to the use of sustainable E-Kinerja applications. In accordance with the concept used by researchers, namely the criteria for successful implementation of the aspect of ensuring productivity and performance analysts. The implementation of e-Kinerja is also faced with several obstacles, including the lack of ability of employees to adopt new innovations or, lack of IT/ICT experts, lack of facilities and infrastructure, and the absence of regulations governing the implementation of E-Kinerja specifically. Therefore, periodic technical guidance is needed with a focus on routine discussions, appointing one of the experts as E-Kinerja admin and providing a more stable internet network.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".