Desra, Melani Hidayati (2025) Data Mining untuk Klasifikasi Tingkat Kecenderungan Fear Of Missing Out pada Mahasiswa Riau menggunakan Metode Naïve Bayes. Other thesis, Universitas Islam Riau.

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Abstract

Social media is growing rapidly, and technology is becoming increasingly integrated into people's daily lives. Observing the phenomenon of today's teenagers who are very fond of using social media, the younger generation is always connected to their social media, engaging in activities such as updating, reading, and commenting on statuses, as well as sharing information on social media. This situation makes teenagers prone to addiction problems. One of the main factors driving high-frequency social media addiction is the fear of missing out on the latest information, which then drives excessive usage behavior. Therefore, a web-based application was developed to assess and determine the level of fear of missing out among students in Riau. The method used is the Naïve Bayes Classifier algorithm, a classification method that employs probability and statistical techniques. Data was collected through a questionnaire administered to Riau students, yielding 170 responses, with 80% of the data used for training and 20% for testing. The results of the model evaluation using the confusion matrix showed an accuracy rate of 85.29%, a precision rate of 82.55%, and a recall rate of 85.85%, indicating that the developed system can accurately predict the level of FOMO among Riau students. Based on the results of the confusion matrix evaluation test, it shows that 64% of the 34 Riau student respondents are in the moderate category of FOMO.

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