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Shafa Arrizqa Az Zahroh
University of Muhammadiyah Sidoarjo, Indonesia

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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
Journal of Artificial Intelligence and Digital Economy Vol. 2 No. 12 (2025): Journal of Artificial Intelligence and Digital Economy
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/jaide.v2i12.1554

Abstract

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.
Co-Authors Irwan Alnarus Kautsar Novia Ariyanti Nuril Lutvi Azizah