EFEKTIVITAS PENGGUNAAN SISTEM INFORMASI KEPEGAWAIAN DALAM MANAJEMEN PNS DI KANTOR WILAYAH KEMENTERIAN HUKUM DAN HAK ASASI MANUSIA LAMPUNG
Bibliographic record
Abstract
The use of an information system is a must in facing complex organizational challenges. Utilization of the Personnel Management Information System aims to improve work efficiency and decision making related to civil servant management. The purpose of this study was to determine the effectiveness of the use of the Civil Service Administration Information System (SIMPEG) for Civil Servants in the Regional Office of the Ministry of Law and Human Rights in Lampung and the obstacles to its use. The method used in this study is naturalistic qualitative, researchers will collect data in a natural way through interviews with key informants, direct field observations and researching related documents. The results showed that the use of Administrative Information Systems in PNS Management at the Regional Office of the Ministry of Law and Human Rights Lampung is still quite effective because it fulfills the components of measuring the effectiveness of information systems in the form of security (confidentiality, availability, integrity) and output. The obstacles encountered when using the Civil Service Information System (SIMPEG) to meet the needs of civil servant management were that corrupt files were found due to application errors, incomplete data, leadership intervention in making decisions that ruled out the information presented by SIMPEG for consideration, and the internet network is unstable to access SIMPEG so that the staffing service process is obstructed. Keywords : Effectiveness; Civil Servant Management; SIMPEG.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".