SISTEM PAKAR MENDIAGNOSA PENYAKIT GIGI MENGGUNAKAN METODE CERTAINTY FACTOR
Bibliographic record
Abstract
Expert system is a computer-based application that is used to solve problems as thought by experts. One problem that can be solved by using an expert system is diagnosing.Teeth are one of the most important organs in the human body. As the only organ that cannot heal itself, the tooth becomes an organ that is highly maintained and cared for during one's life. The teeth are also very important organs in the food processing process. In this study the user chose one of three types of dental disease as their assumptions including periodontitis, gingivitis and dental caries. The system will give questions about the symptoms of dental disease. The certainty factor value of the three types of dental diseases based on user input is 95.9% of gingivitis, 92.7% of periodontitis and 85.9% of dental caries. So it can be concluded that the calculation results indicate the likelihood of users suffering from gingivitis 95.9%. Henceforth the system will provide a handling solution.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".