Bibliographic record
Abstract
Tata kelola Teknologi Informasi merupakan serangkaian strategi TI, perencanaan, kebijakan, praktik penerapan TI, sumber daya, dan aktivitas pengendalian. Tata kelola data membantu organisasi dalam pengelolaan ketersediaan, kegunaan, integritas dan keamanan data sebagai aset berharga di organisasi. Badan Kepegawaian Negara sebagai pengelola data Aparatur Sipil Negara berkomitmen untuk dapat menyelenggarakan layanan manajemen ASN berbasis data melalui pemenuhan kualitas data ASN yang akurat, mutakhir, terpadu dan dapat dipertanggung jawabkan serta mudah diakses dan di bagipakaikan antar instansi melalui satu data ASN. Peningkatan kualitas data ASN menghadapi beberapa isu permasalahan terkait dengan kelengkapan dan keakuratan data, untuk itu diperlukan roadmap strategi untuk penerapan satu data ASN. Penelitian ini menggunakan pendekatan fishbone diagram analysis untuk mengidentifikasi permasalahan, gap analysis untuk menentukan langkah strategis dan analisis risiko untuk dapat menggambarkan risiko disetiap aktivitas strategis. Pendekatan hybrid dan sintesis dilakukan untuk memformulasikan setiap dimensi dalam roadmap strategis serta langkah strategis dalam setiap dimensi. Hasil penelitian menjelaskan bahwa terdapat sembilan dimensi roadmap strategis yaitu peraturan/kebijakan, SDM TIK bidang data, Arsitektur data, datainduk/referensi, standar data, metadata, basis data, kualitas data dan interoperabilitas data. Roadmap strategis didahuliui dengan penentuan visi, misi dan tujuan Satu Data ASN.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".