Classification of Tile Productivity Data Based on Tile Type Using Random Forest Algorithm in Langkat Regency
Bibliographic record
Abstract
This study aims to classify data on the productivity of census in Langkat Regency based on the type of census by using the Random Forest algorithm. Ubinan is a method used to measure the productivity of food crops, and in this study, the data was processed with various variables such as planting area, type of fertilizer, type of pesticide, and production volume. The Random Forest algorithm was used to build a classification model that could predict the productivity of the tares with very high accuracy, reaching 99.58% in the training stage. The model categorizes the productivity of the samples into several levels, namely Very Low, Low, Medium, High, and Very High. The implementation of this system is also equipped with a MATLAB GUI interface, which makes it easier for users to train and test data efficiently. With this system, users can see the prediction results through intuitive visualization. This research is expected to help farmers and policy makers in improving agricultural productivity through data-based analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".