Diagnosa Penyakit Chikungunya Menggunakan Metode Certainty Factor
Bibliographic record
Abstract
Chikungunya is a disease caused by the Chikungunya virus (CHIKV) transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Some of the symptoms are sudden fever, chills, pain in the joints, muscle pain in the neck, shoulders and limbs, rash. Treatment of sufferers is aimed at complaints and symptoms that arise and the drugs given are generally only to relieve existing symptoms, such as giving Antipyretic drugs to relieve fever, Antiemetic drugs to relieve nausea / vomiting, or Analgetics to relieve joint pain only, from these problems many people also do not understand the handling of chikungunya disease, therefore it is necessary to have a system that can collect information to collect information and conduct counseling activities on the treatment of Chikungunya disease to the general public and especially to the elderly to provide knowledge about Chikungunya disease using the Certainty Factor method. The purpose of this research is to provide information and knowledge to the public about Chikungunya Disease Treatment and the Elderly know the medicine for Chikungunya Disease treatment. From the results of applying the certainty factor method to diagnose chikungunya disease, it can be obtained that the diagnosis result is acute chikungunya disease with a confidence level of 95.19%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".