ANALISIS LITERATUR PREDIKSI TREN PENJUALAN E-COMMERCE BERBASIS DATA TIME-SERIES: METODE STATISTIK & MACHINE LEARNING
DOI:
https://doi.org/10.61722/jipm.v3i2.844Keywords:
E-commerce, Prediksi Tren Penjualan, Data Time-Series, Analisis Literature, Machine LearningAbstract
The rapid development of the e-commerce industry in Indonesia has generated a huge amount of time-series sales data, creating a significant opportunity to predict sales trends. This study aims to analyze the existing literature on methods used to predict e-commerce sales trends by utilizing time-series data from databases. The analysis shows that various time-series techniques, such as ARIMA, LTSM, and hybrid models, have been applied with varying degrees of success. Factors such as data volume, data pattern complexity, and the need for real-time prediction play an important role in the selection and effectiveness of prediction methods. Overall, this study concludes that hybrid models and machine learning, especially LTSM, show great potential in improving the accuracy of sales trend prediction in the e-commerce domain.
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