Analysis of Smartphone Addiction of Immanuel Medan Students Using Data Mining Classification Method (Naive Bayes and C4.5) - Berna Susanti Br Tarigan; Resa Jesiulina H; Elvis Sastra Ompusunggu (Universitas Malikussaleh, 2026)
Smartphone addiction among students is a problem that interferes with their concentration while studying, social interactions, and their academic motivation. This study analyzed the level of smartphone addiction among students of SMKS Immanuel Medan using the Naive Bayes classification algorithm and the C4.5 decision tree. This study adopts a comparative quantitative approach using the phases of Knowledge Discovery in Databases (KDD), including data collection, data cleaning, data selection, data transformation, data mining, and evaluation. The research data was collected by distributing questionnaires to 100 students at SMKS Immanuel Medan. The study variables included age, gender, duration of smartphone use, purpose of smartphone use, dominant type of social media, and the level of smartphone addiction as target variables. The classification was carried out using RapidMiner software with a 70:30 training and testing data split. Model evaluation was carried out using a confusion matrix with the parameters of accuracy, precision, recall, and F1 score. The results show that the C4.5 decision tree algorithm gives better results than the Naive Bayes algorithm. The C4.5 algorithm achieved 90% accuracy, 88.9% precision, 80% recall, and 84% F1 score, while the Naive Bayes algorithm achieved 80% accuracy, 80% precision, 66.7% recall, and 73% F1 score. This research contributed to the development of a simple web-based expert system that helps schools assess the level of smartphone addiction among students quickly, objectively, and systematically, so that it can be used as a decision-making tool to monitor smartphone use in the education sector.