Predicted Sales of Industrial Homes Exclusive Anugrah Bean Cake using the Linear Regression Method
Bibliographic record
Abstract
UD. Anugrah Exclusive is a peanut cake home industry in Binjai City that faces monthly sales fluctuations so that it has an impact on the management of raw material stocks. This study aims to build a sales prediction model using a simple linear regression method with sales data for the period January 2021–June 2025. Independent variables are in the form of time (months) and dependent variables are in the form of sales (pouch). The model is implemented in a web-based system using Python and MySQL and evaluated using Mean Absolute Percentage Error (MAPE). The results of the study resulted in a regression equation Y = 349.55 + 2.55X with a MAPE accuracy rate of 9.03%, which is in the very good category. The system built can help business owners estimate raw material needs, avoid the risk of overstock or lack of stock, and develop a more appropriate marketing strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".