Analyzing Consumer Shopping Interest via Social Media Ads with K-Means and C4.5 Algorithm

Jepri Banjarnahor; Jessy Putrionom Hutagalung; Ferdinand Jery Wilkinson Sitorus

doi:10.32736/sisfokom.v13i3.2228

Authors

Jepri Banjarnahor Department of Science and Technology, University of Prima Indonesia
Jessy Putrionom Hutagalung Department of Science and Technology, University of Prima Indonesia
Ferdinand Jery Wilkinson Sitorus Department of Science and Technology, University of Prima Indonesia

DOI:

https://doi.org/10.32736/sisfokom.v13i3.2228

Keywords:

Social media, K-means clustering, C4.5 Algorithm, marketing, consumer shopping interest

Abstract

It is increasingly important to understand how advertisements affect consumers' propensity to shop as social media becomes the primary medium for advertising. This study uses the C4.5 algorithm for classification and K-Means Clustering for data segmentation to examine the level of consumer shopping interest driven by Facebook and Instagram ads. This strategy utilizes information collected from user interactions with ads on these two social media platforms to determine consumer interest trends more precisely. The research findings show that, compared to conventional methods, this combination of techniques can increase the accuracy of predicting consumer purchase intention by as much as 85%. These results not only validate the usefulness of clustering and classification methods in digital advertising data analysis, but also offer insights that companies can apply to optimize their marketing strategies. By understanding more specific consumer segments, companies can target their ads more precisely, thereby increasing conversions and the effectiveness of advertising campaigns. This research makes a significant contribution to the field of data analysis and digital marketing and opens up opportunities for further research in the integration of more sophisticated analysis methods

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