RANCANGAN PENELITIAN MODEL HYBRID DETEKSI COVID-19 MENGGUNAKAN MARINE PREDATORS ALGORITHM (MPA) DAN INTERPOLASI LINIER
Bibliographic record
Abstract
The spread of the COVID 19 outbreak that occurred at the end of 2019 started from the city of Wuhan in China, this outbreak continues to spread to all corners of the world, this outbreak has also reached Indonesia, in Indonesia this outbreak has also spread to all corners of the country, this problem is the focus the government's attention because until now the epidemic cannot be stopped. The problem that occurs is very complex because of the difficulty of controlling the spread that occurs so that more and more residents are confirmed positive for COVID 19 but do not show symptoms in the sufferer, this incident is now called People Without Symptoms (OTG). The spread of the virus through OTGs has played a major role in spreading COVID 19 to other residents. The government has carried out COVID 19 tests on residents, but it still cannot be carried out optimally, this problem is faced by the cost, speed, and accuracy of test data that cannot be obtained optimally. This study proposes to build a hybrid algorithm that can speed up the process of analyzing data on residents who are confirmed positive for COVID 19 through the development of Marine-Predators-Algorithm (MPA) and Linear Interpolation that can be implemented to speed up CT-Scan results of patients' lungs and speed up test results to provide confirmed information. positive for COVID-19 in patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".