Prediction of Mental Health of Elementary School (SD) Students using the Decision Tree Algorithm with K-Fold CV testing in Bone Bolango Regency, Gorontalo Province.

  • Salahuddin Liputo Universitas Muhammadiyah Gorontalo
  • Frangky Tupamahu Universitas Muhammadiyah Gorontalo
Keywords: keyword_1; Mental Health 2;SEHS-S 3;Machine Learning 4; Decision Trees


Mental health is a fundamental component of the WHO definition of health, which means not only being free from disease but also being physically, mentally and socially healthy. Currently, mental health has become a major issue in modern society because if it is good it will enable us to realize our own potential, overcome the normal stresses of life, work productively, and be able to contribute to the society in which we live. In Indonesia, problems related to mental health are related to the lack of mental health detection tools. Meanwhile abroad, much research has been developed regarding mental health detection based on innovative technology using Machine Learning. This research aims to predict mental health using the Social Emotional Health Survey-Secondary (SEHS-S) as a prediction evaluation criterion using Machine Learning with the Decision Tree algorithm method with K-Fold CV testing. The sample in this research was elementary school students in Bone Bolango Regency, Gorontalo Province.



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How to Cite
Liputo, S., & Tupamahu, F. (2024). Prediction of Mental Health of Elementary School (SD) Students using the Decision Tree Algorithm with K-Fold CV testing in Bone Bolango Regency, Gorontalo Province. Journal La Multiapp, 5(1), 19-24.