APPLICATION OF DATA MINING TO PREDICT DISTRO CLOTHING SALES USING THE K-MEANS CLUSTERING METHOD

Shafa Arrizqa Az Zahroh; Nuril Lutvi Azizah; Novia Ariyanti; Irwan Alnarus Kautsar

doi:10.61796/jaide.v2i12.1554

Authors

Shafa Arrizqa Az Zahroh University of Muhammadiyah Sidoarjo, Indonesia
Nuril Lutvi Azizah
nurillutviazizah@umsida.ac.id
University of Muhammadiyah Sidoarjo, Indonesia
Novia Ariyanti University of Muhammadiyah Sidoarjo, Indonesia
Irwan Alnarus Kautsar University of Muhammadiyah Sidoarjo, Indonesia

Vol. 2 No. 12 (2025): Journal of Artificial Intelligence and Digital Economy

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November 5, 2025

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Objective: The research aims to classify distro clothing products at Aldi Store according to sales levels to support more effective inventory and marketing strategies. Method: Data processing was conducted using Google Colaboratory, applying the K-Means Clustering algorithm combined with evaluation metrics including the Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index to determine the optimal cluster structure. Results: The analysis shows that K-Means successfully groups sales patterns with strong cluster performance, indicated by a Silhouette Coefficient of 0.576, a Calinski-Harabasz Index of 19.125, and a Davies-Bouldin Index of 0.308, reflecting high cohesion and clear separation among clusters. Novelty: This study integrates multiple validity indices within a practical retail context, demonstrating a robust clustering approach that enhances customer segmentation accuracy and provides actionable insights for strategic decision-making in product management.

Az Zahroh, S. A., Azizah , N. L., Ariyanti, N., & Kautsar, I. A. (2025). APPLICATION OF DATA MINING TO PREDICT DISTRO CLOTHING SALES USING THE K-MEANS CLUSTERING METHOD. Journal of Artificial Intelligence and Digital Economy, 2(12), 838–844. https://doi.org/10.61796/jaide.v2i12.1554

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