The Utilization of Database for Administration Purposes as a Strategy Facing the New Normal
Bibliographic record
Abstract
The rise of Industry 4.0 has revolutionized work dynamics, particularly evident in the widespread adoption of remote working practices. Employees are no longer confined to traditional office spaces; instead, they have the flexibility to work efficiently from various locations. This study delves into the creation of a remote presence application, named "The SIMPEG-Pres," within the framework of an "e-Office Application." Tailored for Ministry of Agraria and Spatial Planning/National Land Agency employees, this application incorporates remote check-in features, focusing on transparency, informativeness, and georeferenced capabilities. The e-Office application requires seamless extraction of attendance data from the services layer, manifesting as a mobile application. Employing the SIMPEG-Press application in the SIMPEG e-Office system, utilizing an Oracle database and Python backend, the research validates the "published or perished" paradigm and ensures database security. The methodology involves implementing a dummy database and categorizing employee attendance and location zones based on specific parameters, guaranteeing efficient system and user management practices. The study culminates in a comprehensive matrix outlining Land Information System (LIS) development within the BPN environment, analyzed through system development theory. Additionally, the research outlines potential opportunities and challenges in the future trajectory of LIS development, providing valuable insights for both practitioners and scholars. Keywords: Application Programming Interfaces, Location Based Services, Land Information System, Remote Presence Application, Python.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".