Abstract

Technological developments in the Industrial Revolution 4.0 era have had a significant impact on various aspects of life, including the emergence of the phenomenon of smartphone addiction. Smartphones which initially functioned as communication tools have now become an integral part of everyday life. However, excessive use can cause addiction which has a negative impact on an individual's physical, mental health and productivity. This phenomenon is of particular concern among Bina Insan University students, who rely on smartphones for access to information, communication and learning. This study aims to classify the level of smartphone addiction among Bina Insan University students using the KNN algorithm. Research data was obtained through a questionnaire survey using the stratified random sampling method. The KNN algorithm was chosen because of its ability to handle data with different characteristics and produce accurate classification based on the closeness of the data. It is hoped that the results of this research will help Bina Insan University understand the level of student smartphone addiction and provide insight into designing strategies to manage its negative impacts, thereby supporting student well-being and academic performance. The designed model shows good performance with an accuracy of 90%, indicating that 90% of the samples in the test data were predicted correctly. In addition, the high precision and recall for the "Addictive" and "Normal" classes indicate the model's reliable ability to identify samples according to their class. The high F1-score in both classes also shows a good balance between precision and recall, confirming the model's reliability in classifying data.