Integrating K-Means Clustering and Apriori for Data Mining-Based Digital Marketing Strategy For Increasing UMKM: Study Case Stabat City

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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