Rahmayani, Dwita (2026) MODEL MACHINE LEARNING UNTUK MEMPREDIKSI TINGKAT FOMO TERHADAP TREN FASHION BERDASARKAN POLA PENGGUNAAN MEDIA SOSIAL MENGGUNAKAN METODE RANDOM FOREST (STUDI KASUS : UNIVERSITAS MALIKUSSALEH). S1 thesis, Universitas Malikussaleh.

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Abstract

The phenomenon of Fear of Missing Out (FOMO) regarding fashion trends has become an increasingly prevalent behavioral issue among university students, driven by the high intensity of social media usage. This study aims to develop a Machine Learning model using the Random forest algorithm to predict the FOMO levels of Universitas Malikussaleh students based on their social media usage patterns. Primary data collection was conducted via questionnaires distributed to 624 active student respondents. The challenge of class imbalance within the dataset was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), resulting in a balanced training set of 322 samples for each target class (Low, Medium, High). The evaluation of the Random forest model demonstrated a total Accuracy of 62%, with the highest F1-Score of 69% achieved in the medium FOMO level classification. Through Feature Importance analysis and decision tree rules extraction, it was revealed that the influence of fashion trends and direct interaction with fashion content (such as liking activities and viewing OOTDs) were the most dominant indicators triggering FOMO. Conversely, the daily duration of social media usage contributed the least. This study proves that the Random forest algorithm optimized with SMOTE is capable of effectively classifying dynamic psychological behaviors and provides crucial insights for the digital marketing industry as well as student mental health literacy. Keywords: Fear of Missing Out (FOMO), Fashion, Social Media Patterns, Machine Learning, Random forest, SMOTE.

Item Type:	Thesis (S1)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Fakultas Teknik > 57201 - Jurusan Sistem Informasi
Depositing User:	Dwita Rahmayani
Date Deposited:	07 Sep 2026 03:05
Last Modified:	07 Sep 2026 03:05
URI:	https://rama.unimal.ac.id/id/eprint/22475

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