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

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.