Data Analysis of Student Monitoring Using the K-Means Clustering Method
DOI:
https://doi.org/10.64021/ijmst.1.2.50-57.2025Keywords:
Data Mining, Pre-processing, Student Monitoring, K-Means ClusteringAbstract
This study aims to group student monitoring data by focusing on two main variables, namely anxiety level and mood score, using the K-Means Clustering method. The research data was obtained from the Kaggle platform, which contains 1000 rows of data with nine attributes, including Student ID, Date, Class Time, Attendance Status, Stress Level, Sleep Hours, Anxiety Level, Mood Score, and Risk Level. The research process involved several stages, from problem identification, data collection, data cleaning and preprocessing, to the application of the K-Means algorithm. The analysis results showed that the data could be divided into two main groups: Cluster 1 consists of students with low to moderate anxiety levels and high mood scores, while Cluster 2 includes students with high anxiety and low mood scores. These findings provide relevant information for schools or campuses to design more effective psychological support and emotional monitoring programs. Additionally, this clustering method can serve as a foundation for developing an early detection system for psychological issues among students.
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Copyright (c) 2025 Sulistiani, Ahmad Rizky Nusantara Habibi , Adrian Maulana , Hidear Talirongan , Anrom G. Abao , Ahmed Mahmoud Zaki Elmalky , Asno Azzawagama Firdaus (Author)
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