Decision Tree Classification of Fear of Missing Out Levels in Generation Z Technology Product Purchases - Kalfida Eka Wati Siregar; Aidil Halim Lubis (Academic Bright Collaboration, 2026)
Purpose – This study used a Decision Tree to classify score-defined Fear of Missing Out (FOMO) categories associated with Generation Z technology-product purchasing, identify discriminative questionnaire items, and evaluate the stability and interpretability of the classification structure. Methods – The dataset contained 500 respondents and 13 self-developed four-point Likert items. Corrected item-total correlations, Cronbach’s alpha, and a preliminary one-factor exploratory factor analysis were examined. A stratified 80:20 split was used for an unpruned Decision Tree and a cost-complexity-pruned sensitivity model selected by five-fold training-only cross-validation. A deterministic score rule, root-split information gain, model-wide importance, and 100 repeated stratified splits were also evaluated. Findings – The unpruned Decision Tree classified 92 of 100 holdout observations correctly (92.00% accuracy; 92.35% weighted F1; 86.64% macro F1). Training-only pruning reduced the tree from 49 to 35 nodes and produced 93.00% accuracy and an 88.99% macro F1. X1 had the highest primary root-split information gain (0.6805) and ranked first in 74 of 100 repeated splits, whereas X5 ranked first in 26. Repeated-split accuracy averaged 94.13%, but balanced accuracy and macro F1 averaged 88.52% and 88.89%, respectively. The deterministic score rule achieved 100%. Research implications – The Low, Moderate, and High categories were equal-width operational score intervals and have not been externally validated. Preliminary factor analysis supported one broad common factor, but the self-developed instrument still requires independent content, construct, and criterion validation. The target was derived from the same items used as predictors, minority-class performance varied across splits, and the available records did not document ethics approval, guardian consent, or minor assent for participants younger than 18. The findings therefore describe score-category discrimination rather than causal purchasing behaviour or validated psychological severity. Originality – This study integrates interpretable Decision Tree classification, training-only pruning, psychometric diagnostics, deterministic-rule comparison, multiple feature-importance perspectives, and repeated-split stability analysis for technology-purchasing FOMO patterns.