SISTEM PAKAR PENYAKIT TEMBAKAU NA OOGS MENGGUNAKAN METODE CERTAINTY FACTOR BERBASIS WEB
Bibliographic record
Abstract
Pengendalian secara dini penting agar tidak meluasnya penyakit tembakau. Yang terjangkit ketanaman yang masih sehat dan baik. Tetapi terkadang para petani tembakau masih bingung mendiagnosa penyakit apa saja yang menyerang tanaman tembakau dikarenakan gejala yang tidak diketahui dan juga penangananya. disini peran pakar penting untuk membantu para petani untuk mendiagnosa penyakit dan juga bagaimana cara menangani penyakit yang menjangkit tanaman tembakau. Akan tetapi karena keterbatasan waktu dan tenaga terkadang para pakar tidak bisa mendiagnosa semuanya. Oleh karena itu, diperlukan sebuah sistem pakar yang dapat menringankan peran seorang pakar dan memberikan edukasi pengetahuan-pengetahuan umum mengenai penyakit tembakau kepada petani. Tujuan dari penelitian ini adalah merancang dan membangun sebuah sistem pakar yang dapat mendiagnosa penyakit tembakau. berdasarkan gejala-gejala yang telah dimasukkan serta memberikan rekomendasi berupa informasi dan penanganan terhadap penyakit tersebut.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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 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".