PEMBUATAN APLIKASI SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT MATA PADA MANUSIA BERBASIS WEB
Bibliographic record
Abstract
The eye is the organ which gives us the sense of vision, with eye human can recognize people, object, differentiating color and miscellaneous its. Eye is including to part of that sensitive body organ, hence from health of eye have to be taken care of and paid attention with as good as possible in order not to lessen eye performance. By exploiting growth of information technology in computer area hence made an "Making an Application of Expert System For Diagnose Eye Disease At Human Base on Web". This Application aim to provide information concerning types disease of eye at human completely like definition, symptom, medication and cause for the disease of eye. Besides, this application also provide a facility to diagnosed disease of eye. Method which is used in making of this application is method of forward chaining. While for the method of modelling system use flow map (schema emit a stream of document) and UML (Unified Modelling Language). Software which is used in making of this application is programming of PHP5, and MySQL as databases server. With existence of this expert system application expected can water down society in getting information concerning types disease of eye and also can assist society in doing inspection to disease of suffered eye.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.028 |
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".