Prediction of Credit Sales Value with the Naive Bayes Algorithm on Sujase Cell Jakarta
DOI:
https://doi.org/10.62951/ijamc.v1i3.110Keywords:
Customer Interest, Mobile Credit, Naive Bayes, Sales Forecasting, Transaction AnalysisAbstract
Background: The rapid growth of mobile phone usage has significantly increased the demand for prepaid credit services (mobile airtime), creating large volumes of transaction data that require effective analysis for business decision-making. Sujase Cell, a mobile credit retailer in Jakarta, faces challenges in predicting future sales performance and customer purchasing interest due to the accumulation of transaction records over time and the limitations of manual analysis. Objective: This study aims to identify customer purchasing interest and predict mobile credit sales values by implementing the Naive Bayes algorithm as a data mining approach to support sales forecasting and business development strategies. Methods: The research employed a quantitative predictive approach using a private dataset obtained from Sujase Cell. Data collection was conducted through observation and literature review. The dataset consisted of historical mobile credit sales transactions and sales balance records collected during the study period. The data underwent preprocessing stages, including normalization using the Min-Max Scaler technique, followed by data partitioning into training and testing datasets. The Naive Bayes classification method was then applied to analyze sales patterns and generate predictions. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and confusion matrix-based assessment metrics. Several experimental scenarios involving different training-testing ratios and parameter configurations were conducted to determine the most effective predictive model. Results: The findings indicate that the Naive Bayes method successfully identified sales trends and customer purchasing behavior patterns. The best-performing model was obtained using a 90% training dataset and 10% testing dataset, resulting in the lowest prediction error. Experimental results demonstrated that the generated prediction model was capable of following actual sales patterns and producing reliable forecasting outcomes. The implementation of Naive Bayes provides valuable support for sales planning, inventory management, and marketing decision-making at Sujase Cell, enabling the business to improve operational efficiency and anticipate future market demand more effectively.
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