APPLICATION OF THE RANDOM FOREST ALGORITHM IN PREDICTING IMPULSE BUYING BY CONSUMERS ON INDONESIAN MARKETPLACE APPLICATIONS

Ni Nyoman Cory Villdina; Monica Julia  Efata; Etania  Frederika; Muhammad Sony Maulana

doi:10.33480/pilar.v22i1.7893

Authors

Ni Nyoman Cory Villdina Universitas Bina Sarana Informatika
Monica Julia Efata
Etania Frederika
Muhammad Sony Maulana

DOI:

https://doi.org/10.33480/pilar.v22i1.7893

Keywords:

consumer behavior, e-commerce, impulsive buying, machine learning, random forest

Abstract

The development of e-commerce in Indonesia has driven an increase in impulsive buying behavior influenced by emotional and situational factors. This study aims to determine the important factors that influence impulsive buying by applying the Random Forest machine learning algorithm. Data were obtained from 628 online shopping application users aged 18-40 years in Indonesia. The analysis results indicated that the majority of respondents fall into the medium impulsive buying category (67.9%), with a model accuracy of 86.5%. The Hedonic Browsing (HB) variable had the greatest influence on impulsive buying behavior, followed by Utilitarian Browsing (UB) and Time Perspective (TP). These findings confirm that emotional aspects and situational conditions play a more dominant role than rational considerations in driving spontaneous shopping decisions.

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References

Andini, E. A., Maharani, B. A., Apriliani, M. D., Tsabita, K., Pratyasanto, A. A., Keuangan, P., & Stan, N. (2024). ANALISIS TINGKAT PERILAKU KONSUMTIF BELANJA ONLINE DENGAN ALGORITMA K-MEANS ( STUDI KASUS MAHASISWA XXX ). Integrative Perspectives of Social and Science Journal (IPSSJ), 2(1).

Budiman, S., & Wijaya, T. (2023). Mobile app impulsive buying: A situational factors dataset analysis. Data in Brief, 50, 109559. https://doi.org/10.1016/j.dib.2023.109559

Fahrudin, N. U. R. F., Putra, K. R., & Umaroh, S. (2024). Influence of Data Scaling and Train / Test Split Ratios on LightGBM Efficacy for Obesity Rate Prediction. 9(2), 220–234.

Gozali, I., & Pamungkas, H. S. (2025). Impulsive Buying Among Tiktok Users (Study on Gen Z). Digital Innovation: International Journal of Management, 2(3), 210–218. Retrieved from https://doi.org/10.61132/digitalinnovation.v2i3.464

Ketipov, R., Angelova, V., & Doukovska, L. (2023). Predicting User Behavior in e-Commerce Using Machine Learning Predicting User Machine Learning Behavior in Using. (September). https://doi.org/10.2478/cait-2023-0026

Obi, J. C. (2023). A Comparative Study of Several Classification Metrics and Their Performances on Data A Comparative Study of Several Classification Metrics and Their Performances on Data. (February). https://doi.org/10.30574/wjaets.2023.8.1.0054

Oktavia, S. N. (2022). EFL students’ readiness to have online learning during the COVID-19 pandemic (A case at the even semester students of English Education Study Program of Sultan Agung Islamic University in the academic year 2021/2022). Undergraduate Thesis.

Ramadhan, D. R., Abadi, F. M., M S, F. M., & Yudiarso, A. (2024). Multi Metode Dan Multi Perspektif Konstruk Budaya Kesenangan (Indulgence) Terhadap Pembelian Impulsive Online Dengan Machine Learning. 145–164.

Rinonce, E. M., Jannah, M., Amelia, R., Anggun, Z., & Prasetyo, R. (2025). Fear of Missing Out Fuels Impulsive Buying Behavior in Gen Z. Psikologia : Jurnal Psikologi, 10(1), 97–110. https://doi.org/10.21070/psikologia.v10i1.1847

Safaroh, N. (2023). Pengaruh Fear of Missing Out (FoMO), Shopping Enjoymen, dan Hedonic Shopping Motivation Terhadap Impulse Buying di E-Commerce Shopee Pada Waktu Flash Sale. Journal of Economics, Business, &Entrepreneurship, 5, 34–38. https://doi.org/10.29303/alexandria.v5iSpecialIssue.604

Sivakumar, M., & Parthasarathy, S. (2024). Trade-off between training and testing ratio in machine learning for medical image processing. 1–17. https://doi.org/10.7717/peerj-cs.2245

Vidyanata, D., Junianto, Y., & Setiobudi, A. (2024). Hedonic browsing behaviour and its impact on impulsive buying among generation z. Primanomics: Jurnal Ekonomi & Bisnis, 22(2), 34–43.

Wan, X., Zeng, J., & Zhang, L. (2024). Predicting online shopping addiction: a decision tree model analysis. Frontiers in Psychology, 15(January). https://doi.org/10.3389/fpsyg.2024.1462376

Wang, T., & Lin, J. (2025). Machine Learning – Based Random Forest Prediction of Online Shopping Behavior in the Digital Economy. 315–320. https://doi.org/10.1145/3745133.3745186

Yasin, P., Ding, L., Mamat, M., Guo, W., & Song, X. (2025). Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data : Development and External Validation of a Novel Web-Based Risk Calculator with Explainable Artificial Intelligence ( XAI ). (May), 2797–2821.