Integrasi Algoritma Apriori dan K-Means untuk Optimalisasi serta Analisis Pola Pemasaran Suku Cadang Otomotif

Benny Martha Dinata; Eri Zuliarso

doi:10.29408/edumatic.v9i3.31664

Authors

Benny Martha Dinata Program Studi Teknologi Informasi, Unversitas Stikubank Semarang https://orcid.org/0000-0002-2527-7378
Eri Zuliarso Program Studi Teknologi Informasi, Unversitas Stikubank Semarang https://orcid.org/0000-0002-8800-4748

DOI:

https://doi.org/10.29408/edumatic.v9i3.31664

Keywords:

apriori, automotive retail, inventory management, k-means, purchasing patterns

Abstract

The automotive parts industry in Indonesia faces challenges of inefficient inventory management and a lack of understanding of customer purchasing patterns, resulting in overstocking or stockouts that cause financial losses. This study aims to apply association rule mining and K-Means clustering to automotive parts retail transaction data to uncover purchasing patterns and product segmentation to support effective inventory management and marketing strategies. Our research is quantitative in nature with a dataset of 14,165 transactions analyzed using Google Colab-Python. The Apriori algorithm was applied with a minimum support of 1% and confidence of 50%, while K-Means clustering was used for product segmentation with normalized numerical attributes. Association rule mining identified 15 significant rules with the strongest pattern between differential oil and brake fluid (confidence 73.5%, lift ratio 5.515). K-Means produced seven optimal clusters (silhouette score 0.68) that categorized products into premium, fast-moving, slow-moving, and other specific characteristics. The main contribution is an integrated framework that combines clustering to enrich the interpretation of association rules, enabling effective bundling strategies. Practical implications include a cross-selling recommendation system (15-20% revenue increase), differential pricing per cluster, and stock predictions that reduce overstocking by 25% and avoid stock-outs, supporting the digital transformation of data-driven retail management.

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