Sosialisasi Sistem Informasi Orang Dalam Pengawasan (ODP) di Kota Kendari
Bibliographic record
Abstract
Merebaknya Kasus Covid-19 khususnya untuk daerah Kota Kendari membuat masyarakat menjadi resah dan takut. Informasi data-data Orang Dalam Pemantauan (ODP) disetiap kelurahan masih kurang, sehingga masyarakat belum mengetaui berapa orang yang menjadi ODP di setiap kelurahan atau RT/RW dari masing-masing kelurahan. Dalam memecahkan masalah tersebut diperlukan sebuah sistem informasi yang dapat memberikan informasi kepada Lurah atau masyarakat mengenai informasi data-data ODP di setiap Kelurahan, sehingga masyarakat dapat mengetahuinya dan dapat di akses melalui internet/mobile. Dalam pengembangan sistem informasi orang dalam pemantaun dengan menggunakan metode waterfall. Metode ini menggambarkan pendekatan yang cukup sistematis juga berurutan pada pengembangan software. Hasil dari sistem informasi orang dalam pemantauan pihak satuan gugus covid dalam memberikan informasi ke masyarakat mengenai orang dalam pemantauan di setiap kelurahan Kota Kendari.Kata Kunci: sistem, informasi, ODP, Covid-19.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.019 |
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".