<![CDATA[This study aims to analyze adolescents’ educational goal clarity based on the integration of psychometric and digital activity data using a machine learning approach. The main problem addressed in this study is the limited utilization of digital and psychological data to understand adolescents’ educational goal characteristics. The methodology includes data preprocessing, clustering analysis using the K-Means algorithm to identify hidden patterns, and classification using Logistic Regression and Decision Tree to build predictive models. The results show that respondents are grouped into three main clusters: low, medium, and high, reflecting different levels of digital engagement and educational goal clarity. Using an 80:20 data split, the results show that on the test set (20%), Logistic Regression achieved the best performance with an accuracy of 0.95 and ROC-AUC of 0.996, while Decision Tree provided more interpretable patterns with an accuracy of 0.80. The most influential variables include frequency of information searching, duration of accessing educational content, and clustering results. The study concludes that productive digital behavior significantly contributes to adolescents’ educational goal clarity, and the hybrid machine learning approach is effective in combining pattern analysis and prediction.]]>
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