Comparative Analysis of Data Mining Algorithms for the Effectiveness of Information Management in the E-Commerce Industry

Abstract

This study aims to analyze the comparison of several data mining algorithms in order to improve the effectiveness of information management in the e-commerce industry. Along with the rapid growth of e-commerce, information management is a crucial aspect to support informed and fast decision-making. This study uses a qualitative approach with the literature study method (library research), which is to review various relevant scientific publications to identify the advantages, disadvantages, and application of data mining algorithms such as Decision Tree, K-Means, Naive Bayes, and Random Forest in the context of information management. From the results of the literature review, it was found that each algorithm has its own characteristics in terms of accuracy, efficiency, and compatibility with certain types of data that are common in e-commerce such as transaction data, consumer behavior, and product preferences. This research is expected to provide more comprehensive insights for practitioners and academics in choosing the right algorithm to optimize information management. In addition, this study also contributes to the development of technology-based data management strategies that are adaptive to the dynamics of the digital industry