SISTEM INFORMASI TUGAS PEGAWAI UNTUK ADMIN, PENANGGUNG JAWAB DAN PENGENDALI TEKNIS DPMPTSP KABUPATEN BANYUWANGI
Bibliographic record
Abstract
The increasing demand for digital transformation in the public sector has encouraged government institutions to optimize their performance through information systems. This study was carried out at the Investment and One-Stop Integrated Service Office (DPMPTSP) of Banyuwangi Regency still faces challenges in monitoring employee tasks, which are carried out manually and prone to delays, lack of transparency, and imbalance of workloads. This research aims to design a web-based Employee Task Monitoring Information System (SIMANTAP) to support effective and accountable task management. The methodology adopted in this research was the Research and Development (R&D) framework, implemented through the waterfall development model for system development. The data were gathered using several techniques, namely interviews, direct observation, and a review of relevant literature. The system design process involved the preparation of use case diagrams, activity diagrams, sequence diagrams, class diagrams, as well as a database schema, and user interface prototypes using Draw.io and Figma. The outcome of this study is a system design which allows administrators, supervisors, and technical controllers to monitor, assign, and evaluate employee tasks in real time. This system is expected to improve transparency, workload distribution, and performance evaluation within DPMPTSP Banyuwangi. Beyond the conceptual contribution, the design also provides practical value by supporting daily task management in government institutions, making work supervision more efficient and accountable.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".