User Satisfaction Classification of Tiktok Shop Skincare Products Using C4.5 and Random Forest for Recommendation Strategy - Nursalim Nursalim; Muhamad Ziaul Haq; A. Nurul Hidayat; Budi Mulyono (Yayasan Darussalam Bengkulu, 2026)
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.