Data Mining Using K-Means Algorithm for Clustering Snack Sales at CV Sinar Pangan Utama
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".