PENGOLAHAN CITRA UNTUK IDENTIFIKASI KEMATANGAN BUAH JERUK DENGAN MENGGUNAKAN METODE BACKPROPAGATION BERDASARKAN NILAI HSV
Bibliographic record
Abstract
Tanah Karo is one of the mountainous areas that has very cool air, so Tanah Karo is a producer of citrus fruits that produce citrus fruits of good quality. But unfortunately the citrus farmers in the area still use conventional (manual) methods or by seeing with the human eye's eyes in selecting citrus fruits of suitable maturity and service without special knowledge and only from their experience. However, human vision has the limitation that the human eye will experience fatigue. Harvesting unripe citrus fruits results in inappropriate quality of citrus fruit being marketed and if you harvest too ripe citrus fruits, it will cause the citrus fruits to rot quickly when they are distributed to agents or buyers in the market. From the input pattern / image of citrus fruits as training data and training targets, it can identify the ripeness of citrus fruits using the backpropagation method. Based on the citrus fruit image data, it can recognize the pattern of citrus fruit that has a maturity level that matches the digital image using the backpropagation method, with the accuracy of training and testing data being 100%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".