Predictive Analytics on Shopee for Optimizing Product Demand Prediction through K-Means Clustering and KNN Algorithm Fusion - Febima, Mesi; Magdalena, Lena (Journal of Information System and Informatics, 2024)
This study focuses on predictive analysis in the context of the Shopee market, aiming to optimize product demand forecasting through the combination of K-Means clustering and KNN algorithms. With the exponential growth of e-commerce platforms like Shopee, accurately predicting product demand is becoming increasingly important for inventory management and marketing strategies. In this research, we propose a novel approach that combines the strengths of K-Means clustering and the KNN algorithm to improve demand prediction accuracy. By leveraging K-Means clustering to group similar products into two clusters, namely “Low Interest” with 64 data points and “High Interest” with 25 data points, we then apply the KNN algorithm to predict demand within each cluster. The KNN algorithm produces two classifications: Low Sales and High Sales. Based on tests using the KNN algorithm with k values of 3, 5, and 7, it was demonstrated that the product “Soraya Bedsheet Cotton Gold Motif Dallas Ask Grey Tua” can be predicted to fall under “High Sales.” The sales prediction accuracy rate for Shopee marketplace products is 96%. The implications of these findings indicate that the combination of K-Means and KNN algorithms can improve the accuracy of product demand predictions and optimize inventory and marketing strategies.