PENGEMBANGAN WEBSITE SISTEM INFORMASI ADMINISTRASI KEPENDUDUKAN PADA KELURAHAN TUMBANG RUNGAN KOTA PALANGKA RAYA MENGGUNAKAN METODE WATERFALL
Bibliographic record
Abstract
Tumbang Rungan sub-district in Palangka Raya manages population data as part of Population Administration, among others; Death Certificate, Certificate of Marriage / Marriage, Birth Certificate, Transfer Certificate, Disability Certificate, Building Construction Permit (IMB), Land and Building Tax, and Land Certificate. Currently there are still many government agencies that manually process population data, including Tumbang Rungan Village, Palangka Raya City, which still uses paper-based forms. If the listed requirements are incomplete, then the person concerned must go home / return first to complete the missing requirements, until they are sufficient and complete. This is very troublesome and wasteful of energy and other sacrifices. Kelurahan Tumbang Rungan, Palangkaraya City, plans to build a system that aims to assist the community in submitting information and receiving complaints by utilizing web facilities as well as monitoring the correspondence process (which is being processed or completed), and to enable the public to fill in data online. To overcome these problems, a study was made, website based with the stages of research divided into two stages, namely: (1) literature study and (2) software development by applying modified waterfall method which includes four steps namely system analysis, design, implementation and testing. For implementation using PHP in making program code and Mysql as a database to store data. Furthermore, software testing uses Blackbox testing
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".