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Smartphones are an object that is always carried everywhere and cannot be separated from everyday life. Smartphones were created with various purposes and features present in terms of education, entertainment, information, communication, and others. Besides these positive things, smartphones also trigger negative impacts such as wasting time interacting with screens, reducing academic achievement, and even decreasing muscle mass due to lack of movement. The next level is to make people become addicted to smartphones. For early adolescent children, smartphone addictions cause insomnia, social behavior, low self-esteem, and even anxiety. The complexity of anxiety symptoms in children is difficult for adults to understand. So, a way to predict smartphone addiction in early adolescence was initiated with a machine learning method, namely the SVM algorithm. The experiment in this paper is the SVM kernel testing. The best performance of SVM is while using a linear kernel; the accuracy reaches 96.4467%. [ABSTRACT FROM AUTHOR]