Model Prediksi Permintaan Produk pada E- Commerce Menggunakan Pendekatan Big Data dan Analisis Metode Pembayaran
DOI:
https://doi.org/10.55606/juitik.v6i3.2748Keywords:
Big Data, Demand Prediction, E-Commerce, Payment Method, XGBoostAbstract
The rapid development of e-commerce has generated large amounts of transaction and customer behavior data that can be utilized to support business decision-making. One of the important challenges faced by e-commerce businesses is predicting product demand accurately because demand fluctuations can cause overstocking or stock shortages. This study aims to develop a product demand prediction model by utilizing big data analytics and incorporating payment method variables. The study uses a quantitative approach with descriptive and predictive analysis. The data consist of historical transaction records, product information, payment methods, and sales volume. The payment methods considered include credit or debit cards, bank transfers, electronic wallets, and cash on delivery. Data processing is designed to include data collection, cleaning, transformation, exploratory analysis, feature engineering, model development, and evaluation. Several prediction approaches are considered, including ARIMA or SARIMA as a traditional time-series model, XGBoost and Random Forest as machine learning models, and Long Short-Term Memory as a deep learning model. Model performance will be evaluated using Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. The proposed approach is expected to provide a more comprehensive representation of product demand by considering payment behavior as an additional predictive variable. The resulting model can support inventory planning and product availability decisions in e-commerce.
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