Integrating K-Means Clustering and Apriori for Data Mining-Based Digital Marketing Strategy For Increasing UMKM: Study Case Stabat City
Bibliographic record
Abstract
Micro, Small, and Medium Enterprises (MSMEs) or UMKM in Bahasa are play a crucial role in regional economic development, yet they often face challenges in designing effective marketing strategies due to limited access to advanced analytical tools. Digital marketing supported by data mining offers a solution to this problem by enabling more precise customer segmentation and product bundling recommendations. This study aims to integrate K-Means clustering and Apriori association rule mining to develop data-driven marketing strategies for MSMEs in Stabat City, Indonesia, with a specific focus on rice sales data. A dataset consisting of 1,000 rice sales transactions was processed through a multi-stage methodology, including data preprocessing, clustering, and association rule generation. The Elbow and Silhouette methods suggested an optimal cluster number of k = 3, resulting in three distinct customer groups: (1) loyal high-value buyers, (2) price-sensitive buyers, and (3) premium-oriented buyers. Descriptive statistics highlighted differences in average transaction values, purchase frequency, and brand preferences across clusters. Apriori analysis produced the top ten significant association rules, such as {Medium Rice} → {Pandan Wangi Rice} with support = 0.14, confidence = 0.68, and lift = 1.23. Promotional simulations showed that generic discount campaigns could increase sales by approximately 3.0%, whereas targeted bundling strategies yielded smaller short-term gains (+1.53%) but offered stronger long-term potential, particularly for premium-oriented clusters. These findings are consistent with prior international studies, where customer segmentation combined with market basket analysis has proven effective for enhancing digital marketing outcomes. The study concludes that integrating clustering and association rules can provide MSMEs with actionable insights to optimize promotional strategies and improve competitiveness. However, limitations remain, including the relatively small dataset, reliance on manual parameter selection, and simplified modeling assumptions. Future research should expand to multi-sector datasets and explore advanced algorithms to validate and extend these findings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".