Identifying Fear of Missing Out (FOMO) in Adolescents Using K-Nearest Neighbors: An Experimental Study of k-Values and Distance Metrics
DOI:
https://doi.org/10.30871/jaic.v10i3.12879Keywords:
Classification, FOMO, K-Nearest Neighbor, KNN, PsychoinformaticsAbstract
Fear of Missing Out (FOMO) is a psychological phenomenon commonly experienced by teenagers due to the high intensity of social media use, and has the potential to cause emotional and social impacts if not identified early. The main problem in identifying FOMO is its internal nature and the difficulty in measuring it objectively using conventional methods. This research proposes a data mining-based classification approach using K-Nearest Neighbor (KNN) to identify the level of FOMO in adolescents. The dataset was obtained from 136 respondents through a questionnaire that included demographic data and the ON-FoMO scale. The research stages include data preprocessing (encoding and Min-Max normalization), data splitting using stratified holdout (80:20), and experiments varying K (3–19) and distance metrics (Euclidean, Manhattan, Chebyshev). The experimental results show that the combination of Euclidean distance with K=11 yields the best performance with an accuracy of 85.71%, ROC AUC of 0.786, Precision–Recall AUC of 0.826, and sensitivity of 100%. The experimental results indicate that the selection of the K parameter and the distance method significantly affect classification performance. Overall, this study concludes that the KNN algorithm with the optimal configuration is effective as an initial screening method for the level of FOMO in adolescents in an systematic and data-based manner.
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