Implementasi Sistem Pakar Diagnosa Penyakit Tifus dengan Metode Certainty Factor
Why this work is in the frame
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Bibliographic record
Abstract
Typhoid fever is a significant health issue caused by the Salmonella Typhi bacteria, leading to symptoms such as fever, abdominal pain, diarrhea, muscle pain, and serious complications if not treated promptly. A common challenge faced by society is limited access to medical professionals, especially in remote areas, and delays in recognizing symptoms. To address this problem, this study designs and implements a web-based expert system using the Certainty Factor (CF) method, which helps diagnose typhoid fever quickly and accurately. The Certainty Factor method is used to calculate the certainty level of the symptoms experienced by the patient, providing a diagnosis result in the form of early-stage typhoid, mild typhoid, or severe typhoid. The system was developed using PHP programming language and MySQL database, and tested at RSUD Djoelham Binjai City. The research data was obtained from patients at RSUD Djoelham Binjai with a case study on patient number 22. The processing of symptoms through Certainty Factor calculation showed that the patient is most likely to have severe typhoid with a certainty value of 0.9443 or 94.43%. This result proves that the Certainty Factor method can be used to assist in providing an accurate early diagnosis of typhoid fever with a high degree of accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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