Application of The Random Forest Algorithm in Classifying the Tendency of Impulsive Purchasing Behavior Among Gen Z Consumers in E-Commerce Based on Flash Sale Features
DOI:
https://doi.org/10.35335/jict.v17i1.329Keywords:
e-commerce, RANDOM FOREST, KLASIFIKASI, FLASH SALE, cashbackAbstract
The rapid growth of e-commerce in Indonesia, particularly on Shopee, has significantly influenced consumer behavior through promotional strategies such as flash sales. This study aims to classify impulsive buying tendencies among Generation Z, identify key influencing factors, and develop a web-based classification system for behavioral analysis. A quantitative data mining approach was applied using the Random Forest algorithm. The dataset consisted of 420 Gen Z respondents collected through a Likert-scale questionnaire using purposive sampling, and model evaluation was conducted using 10-fold cross-validation to ensure reliability. The results show that the Random Forest model achieved an accuracy of 83.16%, outperforming Decision Tree (78.42%) and Logistic Regression (75.08%), indicating its effectiveness in handling complex behavioral patterns. Feature importance analysis revealed that limited stock availability (39.85%) and discount magnitude (33.21%) are the most dominant factors influencing impulsive buying behavior, followed by promotional duration and notification attractiveness. These findings emphasize the role of urgency and scarcity in driving impulsive purchases among Gen Z consumers. Additionally, a web-based system was developed using the Flask framework in Python to support automated data processing, model training, and visualization of results. The system enables real-time behavioral analysis and decision support for digital marketing strategies. Overall, the study demonstrates that machine learning, particularly Random Forest, provides a more accurate and objective approach for analyzing impulsive buying behavior compared to conventional statistical methods, while also offering a practical tool for e-commerce analytics and strategy optimization.
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