Image Processing for Freshness Identification Tilapia Using Backpropagation Algorithm (Case Study: Binjai City Food Security and Agriculture Office)
Bibliographic record
Abstract
Tilapia (Oreochromis niloticus) is a type of fish that comes from rivers and lakes that connect the river. Tilapia was imported to Indonesia officially by the Freshwater Fisheries Research Institute in 1969, Tilapia fish breeds in Indonesia come from Taiwan as for the dark color with vertical stripes as many as 6-8 pieces and the Philippines which is red. The problems faced today related to testing the level of freshness still use conventional methods, namely by only seeing and sorting fish by sight or sight only. This can certainly cause errors in choosing fish for ordinary people or who do not have expertise in choosing fresh fish. For this reason, a system is needed that can identify the freshness of tilapia using digital image management. Many methods are used in identifying an image, one of which is used by using the Backpropagation method.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".