SELEKSI FITUR FORWARD SELECTION PADA ALGORITMA NAIVE BAYES UNTUK KLASIFIKASI BENIH GANDUM
Bibliographic record
Abstract
Abstract - Wheat (Triticum aestivum L) is one of the staple food ingredients besides rice. The demand for the wheat in the world until 2020 is estimated to increase by 1.6% per year. The data processing for wheat seeds has been done a lot, one of them is by using data mining classification techniques. The feature selection is used before the classification process to optimize the accuracy values from the classification results. The feature selection used in this research is forwarding the selection which is applied to the Naive Bayes algorithm to classify the wheat seeds. The results of this study indicate that the value of the accuracy and the wheat classification after using the feature selection has a higher value of 93.81% compared to the condition before using the feature selection of 90.48%. The precision results also increased from 91.49% to 94.81%. Keywords: Forward Selection, Naive Bayes, Classification, Gandum .
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".