Analisis Pola Penjualan Baju Menggunakan Metode Klasifikasi NaiveBayes
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Abstract
The highly dynamic growth of the fashion industry makes consumer demand patterns for clothing difficult to predict. This situation demands a data analysis model capable of identifying sales patterns more accurately so that business owners can make informed decisions regarding stock management, production planning, and marketing strategies. This study aims to analyze clothing sales patterns using the Naive Bayes classification method, a simple yet effective probabilistic algorithm for processing high-dimensional data. The sales dataset was obtained through transaction records, including attributes such as clothing category, price range, purchase time, customer gender, and product demand level. The data was then cleaned, transformed, and divided into training and testing data. The Naive Bayes model was applied to classify sales potential into several classes, such as "high," "medium," and "low." Evaluation results using accuracy, precision, recall, and F1-score metrics demonstrated that Naive Bayes was able to classify sales patterns with stable performance, with an average accuracy of 80–90%, depending on the attribute variations used. Further analysis revealed that product category and purchase time were the most influential attributes in predicting sales levels. These findings confirm that Naive Bayes can be an efficient method for processing large, low-complexity sales data. This research contributes to the development of a data analysis model that can be implemented as a decision support system for fashion companies, particularly in determining more targeted marketing strategies and optimizing inventory management.
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