Sistem Informasi Daftar Urut Kepangkatan (Duk) Pegawai Pada Kantor Dinas Pemberdayaan Perempuan Dan Keluarga Berencana Kabupaten Soppeng
Bibliographic record
Abstract
Penelitian bertujuan untuk mengembangkan sebuah sistem yang dmampu menghasilkan informasi Daftar Urut Kepangkatan (DUK) Pegawai di Kantor Dinas Pemberdayaan Perempuan dan Keluarga Berencana Kabupaten Soppeng yang dapat mengatasi permasalahan pengolahan data kepegawaiaan pada sistem yang lama, terutama pembuatan DUK pegawai. Dalam penelitian ini digunakan metode Waterfall untuk pengembangan sistem informasi DUK pegawai dan menjadi dasar uraian tahapan penelitian yang terdiri dari tahap analisis, tahap desain, tahap implementasi dan tahap pengujian sistem.. Pada tahap analisa sistem, data yang dikumpulkan dianalisis dengan metode deskripsi. Hasil analisis selanjutnya dijadikan acuan untuk merancang sistem dengan menggunakan Data Flow Diagram (DFD). Hasil dari tahap perancangan akan diimplementasikan dengan menggunakan bahasa pemrograman Visual Basic 6.0 dengan perangkat lunak database berupa Ms. Access 2013. Hasil pengujian dengan menggunakan metode pengujian black-box terhadap sistem dengan menguji fungsi-fungsi sistem menghasilkan nilai sebesar 100%, artinya sistem telah berfungsi sesuai dengan kebutuhan pihak Kantor Dinas Pemberdayaan Perempuan dan Keluarga Berencana Kabupaten Soppeng
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".