Analisis dan Evaluasi Regulasi Pedoman Penerapan Sistem Informasi Kearsipan Dinamis Terintegrasi (SRIKANDI)
Bibliographic record
Abstract
Peraturan Arsip Nasional Republik Indonesia (ANRI) Nomor 4 Tahun 2021 tentang Pedoman Penerapan Sistem Informasi Kearsipan Dinamis Terintegrasi (SRIKANDI) bertujuan untuk mengoptimalkan pengelolaan arsip dalam sistem pemerintahan berbasis elektronik (SPBE). Penelitian ini menganalisis dan mengevaluasi regulasi tersebut berdasarkan lima dimensi evaluasi peraturan perundang-undangan: Pancasila, ketepatan jenis peraturan, harmoni regulasi, kejelasan rumusan, dan efektivitas implementasi. Hasil analisis menunjukkan adanya disharmoni antara Peranri No. 4 Tahun 2021 dengan Peraturan Presiden No. 95 Tahun 2018, terutama terkait definisi pengguna dan cakupan penerapan sistem. Selain itu, efektivitas implementasi SRIKANDI masih belum optimal karena keterbatasan infrastruktur, rendahnya tingkat adopsi oleh perguruan tinggi negeri serta BUMN/BUMD, serta kurangnya integrasi dengan aplikasi lain dalam ekosistem SPBE. Dari aspek kejelasan rumusan, beberapa ketentuan dalam peraturan ini juga masih ambigu dan dapat menimbulkan multitafsir. Oleh karena itu, penelitian ini merekomendasikan harmonisasi regulasi, penyederhanaan ketentuan teknis, penguatan kompetensi sumber daya manusia, serta percepatan integrasi SRIKANDI dengan sistem pemerintahan lainnya guna meningkatkan efektivitas pengelolaan arsip dinamis secara 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".