Penerapan Sistem Pakar Menentukan Covid-19 Dengan Metode KNN (K Nearest Neighbor) Berbasis Web (Studi Kasus : RSU Sylvani)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Covid-19 adalah virus yang baru muncul di wuhan pada akhir tahun 2019. Gejala yang di timbulkan oleh covid-19 bervariasi antara suhu tubu meningkat, demam, batuk dan lain nya. Untuk mengatasi faktor ketidakpastian dalam mendiagnosis gejala covid-19, system pakar dirancang untuk menemukan kasus serupa mengenai covid 19 tersebut. Gejala-gejala akan dimasukkan dan dicocokan dengan data penelitian untuk diolah dengan data latih, yaitu data lama pasien yang telah terdiagnosi. suatu sistem yang dapat mencegah sejak dini, sehingga membantu mengatasi penyakit yang disebabkan oleh virus covid-19 lebih dini. Subjek penelitian ini adalah sistem pakar untuk menentukan covid-19. Tahap pengembangan sistem dimulai dengan menganalisis kebutuhan sistem, merancang sistem, antara lain membangun basis pengetahuan, pengambilan tabel keputusan, tabel aturan, memonitor kesimpulan, merancang aliran data, diagram relasional entitas yang kemudian melakukan implementasi dan pengujian. dari sistem. Dengan black box test dan alpha test. Hasil penelitian menunjukkan bahwa aplikasi layak dan bermanfaat
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it