USER SATISFACTION CLASSIFICATION OF TIKTOK SHOP SKINCARE PRODUCTS USING C4.5 AND RANDOM FOREST FOR RECOMMENDATION STRATEGY
Abstract
Background: TikTok Shop has become an important social commerce platform for skincare purchases; however, product recommendations are not always perceived as relevant by users. A data-driven satisfaction classification model is therefore needed to support more targeted recommendation strategies.
Method: This study used a quantitative approach involving 150 TikTok Shop users who had purchased skincare products. Data were collected through an online questionnaire containing 14 Likert-scale items and three recommendation-preference items. Instrument quality was evaluated using corrected item-total correlation and Cronbach Alpha. The C4.5 decision tree and Random Forest models were evaluated using stratified 10-fold cross-validation.
Results: All 14 items were valid, with item-total correlations ranging from 0.619 to 0.881, and the overall Cronbach Alpha was 0.969. The satisfaction classes were balanced, consisting of 75 satisfied and 75 unsatisfied respondents. Information gain analysis identified product delivery as the most influential attribute, with a gain value of 0.4551. C4.5 achieved 85.33% accuracy, while Random Forest achieved 83.33% accuracy.
Conclusion: C4.5 provided competitive performance and stronger interpretability than Random Forest for this dataset. The resulting classification rules can be used to prioritize delivery reliability, application usability, and product quality in skincare recommendation strategies.
References
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Fajriah, N., & Sarma, M. (2025). Analisis repurchase intention produk skincare lokal melalui live commerce TikTok dengan customer satisfaction sebagai variabel intervening. Jurnal Manajemen dan Organisasi, 16(1), 78-95. https://doi.org/10.29244/jmo.v16i1.62596
Farhana, N., Okprana, H., & Sormin, R. K. (2022). Analisis tingkat kepuasan pelanggan pada aplikasi TikTok Shop dengan metode algoritma C4.5. SmartEDU: Buletin Education, 1(3), 101-111.
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson.
Kusrini, & Luthfi, E. T. (2009). Algoritma data mining. Andi.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Purwanto, E., et al. (2025). Comparative analysis of classification models for sales prediction in e-commerce: Decision Tree, Random Forest, SVM, Naive Bayes, and KNN. Jurnal Teknik Informatika (JUTIF).
Putri, N., Prasetya, Y., Handayani, P. W., & Fitriani, H. (2023). TikTok Shop: How trust and privacy influence Generation Z's purchasing behaviors. Cogent Social Sciences, 10(1). https://doi.org/10.1080/23311886.2023.2292759
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann.
Ricci, F., Rokach, L., & Shapira, B. (Eds.). (2022). Recommender systems handbook (3rd ed.). Springer.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.
Copyright (c) 2026 Nursalim Nursalim, Muhamad Ziaul Haq, A. Nurul Hidayat, Budi Mulyono
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The authors who publish in the journal agree to the following terms:
- The authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- The authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- The authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The authors warrant that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third-party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
Jurnal Ekonomi dan Bisnis Manajemen Syariah (SEMB-J), Yayasan Darussalam Bengkulu is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at https://siducat.org/index.php/sembj/.
