Sistem Informasi Bank Data Proyek Dinas Pekerjaan Umum Kota Binjai
Bibliographic record
Abstract
Sistem informasi bank data merupakan salah satu sistem informasi yang terdapat pada Dinas PU Kota Binjai, sistem informasi ini bertujuan untuk mengelola data-data para pemborong. Sistem informasi bank data proyek dalam pengolahan dan penyimpanan datanya masih bersifat manual belum menggunakan software aplikasi-aplikasi khusus yang menangani proses pendataan pemborong, oleh karena itu diperlukan adanya suatu sistem informasi berbasis komputerisasi khususnya dengan mengembangkan sistem informasi data proyek dimaksudkan guna mempermudah dalam pengolahan data proyek sampai pada tahap pembuatan laporan data proyek secara periodik. Sistem informasi data proyek mencakup pengolahan data-data pemborong, tabel proyek dan pengerjaan proyek. Adapun proses yang dilakukan untuk mengembangkan sistem informasi data proyek yaitu dengan menggunakan metode dan perancangan dilakukan dengan membuat flowchart, dan data flow diagram (DFD). Setelah melewati tahapan implementasi diperoleh hasil, yaitu keamanan data lebih terjamin karena sudah dilengkapi dengan proses validasi user, selain itu proses pengolahan bank data lebih cepat, penyimpanan data lebih rapi, dan dalam pembuatan laporan waktu yang dibutuhkan lebih singkat dibandingkan sebelumnya.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.040 |
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".