Classification of Tile Productivity Data Based on Tile Type Using Random Forest Algorithm in Langkat Regency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it