SISTEM INFORMASI PENDAFTARAN PERNIKAHAN PADA KANTOR CATATAN SIPIL DI KOTA BINJAI
Bibliographic record
Abstract
The Population and Civil Registry Office of Binjai City is a government agency which is engaged in providing services to the community. In running a marriage registration service, the binjai city Population and Civil Registry service serves Marriage registration for about 10 days because the system used is still not computerized or still manual with a collection of document files. So it takes a very long time in the process. Therefore to achieve less effective and efficient time in registration. Then the making of an online Marriage Registration Information system will be made which will greatly facilitate the marriage registrar's administrators. Using a website base with the programming language used is PHP. So that it will be easy to use many kinds of devices, both laptops, computers, mobile phones, and smartphones. The information system is made according to procedures in the Population and Civil Registry Office of Binjai City. Starting from registration until the issuance of a registered certificate after that can provide activity reports to the Department of Population and Civil Registration binjai city through the website without having to come to the Office of Population and Civil Registration. The results of this thesis are in the form of a Binjai City Marriage Registration Information System. With such a solution the time in service will only take faster so that it becomes maximal.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.030 |
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".