Data Mining Using K-Means Algorithm for Clustering Snack Sales at CV Sinar Pangan Utama
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
Sales activities are a fundamental component of a company’s operations in achieving profitability. CV Sinar Pangan Utama, a company specializing in the production of snacks such as morena, pang pang, amazon, and kue bawang, faces challenges related to inventory surplus and limited insights into consumer behavior. This study aims to apply data mining techniques, specifically the K-Means clustering algorithm, to analyze sales data and identify product groupings based on sales performance. By classifying products into clusters of high and low demand, the company can derive actionable insights to optimize production planning, inventory management, and marketing strategies. The research utilizes sales data spanning from January to December 2023 and is implemented using a PHP and MySQL-based application. The findings are expected to contribute to more efficient decision-making processes by uncovering purchasing patterns, thereby enhancing the company’s responsiveness to market demand and improving overall business performances
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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