STRATEGI KEBIJAKAN PENGUATAN PELAYANAN PUBLIK DAN PENGAWASAN PERIZINAN BERUSAHA DENGAN APLIKASI BERBASIS TEKNOLOGI INFORMASI
Bibliographic record
Abstract
Dalam era digitalisasi yang semakin pesat, penguatan pelayanan publik dan pengawasan perizinan usaha menjadi sangat penting untuk meningkatkan efisiensi dan transparansi dalam pemerintahan Kota Lhokseumawe. Penelitian ini bertujuan untuk menganalisis strategi kebijakan yang dapat diterapkan untuk memperkuat pelayanan publik melalui penggunaan aplikasi berbasis teknologi informasi. Dengan fokus pada aplikasi ︎OSS RBA dan SI CANTIK CLOUD, penelitian ini mengeksplorasi tantangan yang dihadapi, seperti kesalahan cetak, lamanya waktu pelayanan, dan kurangnya pemahaman pemohon terhadap alur proses. Melalui pendekatan evaluatif, penelitian ini merekomendasikan langkah-langkah perbaikan yang meliputi pengembangan sistem yang lebih andal, peningkatan kapasitas sumber daya manusia, serta penerapan langkah-langkah keamanan siber yang tepat. Diharapkan, dengan implementasi strategi kebijakan yang efektif, pelayanan publik dapat ditingkatkan, dan pengawasan perizinan usaha dapat dilakukan secara lebih transparan dan akuntabel, sehingga mendukung pertumbuhan ekonomi dan investasi di daerah.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.015 |
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".