SISTEM PAKAR MENDIAGNOSA PENYAKIT COVID-19 DENGAN MENGGUNAKAN METODE CERTAINTY FACTOR
Bibliographic record
Abstract
COVID-19 is an infectious disease caused by a newly discovered type of coronavirus. The WHO (World Health Organization) reported that this virus first appeared on December 31, 2019 and the country that was first confirmed was China, precisely in the city of Wuhan. Indonesia became one of the confirmed countries after President Jokowi and Minister of Health Terawan Agus Putranto on Monday, March 2, 2020. Most people who are exposed to COVID-19 experience symptoms such as: fever, respiratory tract infection, loss of sense of smell, coughing runny nose, headaches, sore throats, loss of sense of taste, and nausea.. Previous research is a science to find comparisons and results to find new inspiration for research. Research methodology is a scientific process or method to obtain data to be used for research purposes. Methodology is also a theoretical analysis of a method or method, research is a systematic investigation to increase a number of knowledge. Based on the results of the CF calculation, the value obtained for the Covid-19 disease from the calculation results above can be seen the level of confidence from the results of the diagnosis of the Covid-19 disease, which is 0.97 x 100%, which is 97% with the results obtained, the system identifies that the patient is Covid-19 negative. Based on the results of the analysis and design that have been achieved, it can be applied to apply an expert system application to diagnose Covid-19 disease, where in this application the user can enter and find out the types of symptoms of the Covid-19 disease that the user has and can find out the right handling solution to help dealing with 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".