Data Mining Analysis of the Influence of Social Media on Students' Sleep Hours and GPA Using the Cluster Method

Abstract

This study aims to analyze the effect of social media usage and sleep duration on students' Grade Point Average (GPA) using the clustering method in data mining. Social media has become an integral part of students' lives, but excessive use can have a negative impact on sleep time and, indirectly, on academic achievement. Using the clustering method, this study groups students based on their social media usage patterns, sleep hours, and GPA to identify groups with certain characteristics. Data collected from 520 students were analyzed using the K-Means clustering algorithm, which resulted in three main groups: a group with high social media usage and low GPA, a group with a balanced sleep pattern and moderate GPA, and a group with adequate sleep time and high GPA. The results of the analysis showed that students with high social media usage tend to have lower sleep hours and lower GPA than students who have sufficient sleep duration. This study is expected to be a basis for campuses to develop programs to improve student welfare, especially in regulating social media usage and improving sleep quality. The data used in this study uses data from the Kaggle.com platform which provides various types of data worldwide. This research is expected to provide insight for students, lecturers and people with regard to this method.