Persepsi Pegawai Unit Kearsipan SKPD terhadap Aplikasi Sistem Informasi Kearsipan Daerah (SIKEDA) dan Jaringan Informasi Kearsipan Daerah (JIKEDA) di Pemerintahan Kota Bukittinggi
Bibliographic record
Abstract
AbstractIn this paper we discuss the SKPD Filing Unit Employee Perception of the Regional Archival Information System Application (SIKEDA) and the Regional Archival Information Network (JIKEDA) in the City Government of Bukittinggi. The purpose of this study is to determine the competence and optimal shield of SKPD employees in inputting files to the SIKEDA application.This type of research is descriptive research with a qualitative approach. The location of this study was carried out in 5 SKPD Institutions in Bukittinggi, namely: (1) the Library and Archives Office of Bukittinggi City, (2) the Education and Culture Office of the City of Bukittinggi, (3) the Youth and Sports Pariwasata Service of the City of Bukittinggi, (4) the Health Service City of Bukittinggi, (5) Social Service of the city of Bukittinggi. The object of the study was SKPD employees in five government agencies in the city of Bukittinggi. Writing this paper aims to describe (1) To describe the optimization of the use of SIKEDA and JIKEDA applications by Admin node SIKEDA in supporting records management in the city administration of the City of Bukittinggi; (2) To describe the competencies possessed by HR in SKPD in utilizing information technology in the application of SIKEDA and JIKEDA applications in the City Government of Bukittinggi.Data was collected by observation and direct interviews with SKPD employees in the Bukittinggi city government and literature studies in the application of electronic records in the government of the City of Bukittinggi.Based on the discussion, it can be concluded that the First Employee in the SKPD in the City Government of Bukittinggi is still not optimal in inputting the archive to the sikeda Second application.iKeywords: optimization, competence, electronic archives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".