Sistem Informasi Kinerja Pegawai Non-ASN Berbasis Web pada Dinas Kependudukan dan Pencatatan Sipil Kabupaten Situbondo
Bibliographic record
Abstract
The Population and Civil Registration Office of Situbondo Regency faces challenges in improving the efficiency of recording and reporting the performance of Non-ASN employees, which has so far been conducted through temporary notes and documents. This approach is prone to delays, input errors, and suboptimal data management. To address this issue, this study aims to design a web-based information system capable of recording, managing, and displaying Non-ASN employee performance reports in a structured and real-time manner. The system is developed using PHP programming language and MySQL database to support dynamic and integrated data management. The system development follows the V-Model approach, which emphasizes verification and validation at each development stage, including requirement analysis, system design, module design, implementation, unit and integration testing, and maintenance. The result of this study is an information system that facilitates performance data entry, accelerates report generation, and improves accuracy and transparency in evaluating Non-ASN employee performance. In addition, the system is designed with a responsive web interface that is easy for stakeholders to use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.017 |
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".