STRATEGI KEBIJAKAN PENERAPAN MANAJEMEN TALENTA DALAM PENGEMBANGAN KARIER ASN MELALUI PENILAIAN KINERJA DAN POTENSI DI KABUPATEN INDRAMAYU
Bibliographic record
Abstract
Reformasi birokrasi menuntut pengembangan karier ASN berbasis kinerja dan potensi melalui penerapan Manajemen Talenta. Pemerintah Kabupaten Indramayu menghadapi sejumlah tantangan, antara lain keterbatasan regulasi teknis, pemetaan potensi dan talent pool yang belum optimal, penilaian kinerja yang masih administratif, minimnya assessor tersertifikasi, keterbatasan infrastruktur teknologi, serta budaya organisasi yang belum berorientasi kinerja tinggi. Untuk mengatasi hal tersebut, diperlukan strategi kebijakan berupa penyusunan regulasi teknis, pemetaan talenta berbasis 9-Box Grid, succession planning, pemberian reward and punishment, serta pengembangan talent pool digital. Selain itu, penguatan dilakukan melalui digitalisasi data ASN, integrasi dengan SI-ASN BKN, penerapan Digital Talent Management System, peningkatan kapasitas melalui pelatihan dan penyediaan assessor, serta menjadikan pimpinan OPD sebagai role model budaya kinerja. Artikel ini merekomendasikan percepatan implementasi Manajemen Talenta guna memperkuat sistem merit, meningkatkan kualitas pengambilan keputusan berbasis data, serta mewujudkan birokrasi yang profesional, akuntabel, dan berorientasi pelayanan publik.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".