Sales Level Analysis Based on Price and Transaction Volume Using the Naïve Bayes Algorithm at Onten BSD - Nasria Ariska Putri; Belsana Butar Butar; Kartika Mariskhana (Sean Institute, 2026)
Sales performance is an important aspect to consider in inventory management and business decision-making. This study aims to examine the relationship between price categories and sales levels and to implement the Naive Bayes algorithm for classifying product sales growth at Onten BSD. The study utilized 414 sales records, which were processed through several stages, including data cleaning, data transformation, and label encoding. The dataset was subsequently divided into training and testing sets using an 80:20 ratio through the train-test split method. Model performance was evaluated using a Confusion Matrix, Accuracy, Precision, Recall, F1-Score, and Classification Report. The results indicate that products in the Cheap category had the highest average sales volume, reaching 93.41 units. The implementation of the Naive Bayes algorithm achieved an Accuracy of 85.54%, Precision of 88.82%, Recall of 85.54%, and F1-Score of 85.26%. These findings demonstrate that the Naive Bayes algorithm can effectively classify product sales Growth and provide useful insights into sales conditions. Therefore, its application can support Onten BSD in managing products more appropriately and making more informed business decisions.