ANALISIS PENGELOLAAN ARSIP DINAMIS DI DINAS PERPUSTAKAAN DAN KEARSIPAN KABUPATEN SIJUNJUNG DALAM MEWUJUDKAN TERTIB ADMINISTRASI KEARSIPAN
Bibliographic record
Abstract
Pengelolaan arsip dinamis merupakan komponen penting dalam mendukung tertib administrasi dan kelancaran proses birokrasi pada instansi pemerintah. Penelitian ini bertujuan untuk menganalisis implementasi pengelolaan arsip dinamis di Dinas Perpustakaan dan Kearsipan Kabupaten Sijunjung serta mengidentifikasi faktor pendukung dan penghambat dalam penerapannya. Metode penelitian yang digunakan adalah kualitatif deskriptif melalui penelaahan dokumen, observasi, dan analisis praktik pengelolaan arsip yang berlangsung di lingkungan instansi. Hasil penelitian menunjukkan bahwa proses pengelolaan arsip telah mengikuti tahapan dasar records life cycle meliputi penciptaan, penggunaan, pemeliharaan, dan penyusutan arsip. Namun, masih ditemukan kendala berupa keterbatasan sumber daya manusia yang kompeten, fasilitas penyimpanan yang belum optimal, serta pemanfaatan teknologi kearsipan yang belum sepenuhnya terintegrasi dengan sistem digital. Kondisi ini berdampak pada keterlambatan temu kembali arsip dan belum maksimalnya efektivitas layanan administrasi. Dengan demikian, peningkatan kompetensi pegawai, penyediaan sarana kearsipan standar, dan penguatan digitalisasi arsip diperlukan untuk mewujudkan sistem administrasi yang akuntabel, efektif, dan sesuai dengan prinsip kearsipan nasional.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".