Inovasi Pengelolaan Arsip di Era Digital Melalui Implementasi SIAR di Bappedalitbang Kabupaten Nias
Bibliographic record
Abstract
Proyek ini bertujuan untuk mengembangkan dan mengimplementasikan aplikasi Sistem Informasi Arsip Surat (SIAR) di Badan Perencanaan Pembangunan, Penelitian, dan Pengembangan Daerah (Bappedalitbang) Kabupaten Nias, guna meningkatkan efisiensi pengelolaan arsip surat masuk dan keluar. Metode yang digunakan mencakup analisis kebutuhan pengguna, perancangan sistem, pengembangan prototipe, implementasi, serta evaluasi kinerja aplikasi. SIAR didesain untuk menggantikan sistem manual yang tidak efisien, memberikan akses cepat dan transparan terhadap arsip melalui digitalisasi. Hasil menunjukkan bahwa SIAR berhasil meningkatkan efisiensi operasional dengan mempercepat proses pencarian dan disposisi surat, mengurangi beban administratif, dan meningkatkan produktivitas pegawai. Selain itu, aplikasi ini juga meningkatkan akuntabilitas dan keamanan data arsip, dengan sistem enkripsi dan kontrol akses. Sebagai kesimpulan adalah bahwa implementasi SIAR di Bappedalitbang Kabupaten Nias memberikan dampak positif dalam hal peningkatan kualitas kerja, aksesibilitas arsip, serta keamanan data, yang pada akhirnya mendukung tercapainya tujuan organisasi. Pelatihan berkelanjutan dan pemeliharaan sistem tetap diperlukan untuk memastikan keberhasilan jangka panjang aplikasi ini.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.025 |
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".