SUPPORT VECTOR MACHINE ALGORITHM FOR EARLY DETECTION SYSTEM FOR MENTAL EMOTIONAL DISORDERS IN ADOLESCENTS
DOI:
https://doi.org/10.37859/seis.v5i1.8351
Abstract
A mental-emotional disorder is a condition characterized by emotional fluctuations that, if left untreated, might progress into an abnormal state. In Indonesia, the treatment of mental problems is infrequently conducted due to a scarcity of psychiatric personnel and the high expenses associated with comprehensive mental health therapy and treatment. An early detection system for mental-emotional illnesses in teenagers was developed by implementing the Support Vector Machine (SVM) algorithm as a solution to this issue. The Support Vector Machine algorithm is a very accurate classification approach. This study utilizes data that is categorized into two distinct groups: anxiety and depression. The data is partitioned in an 80:20 ratio, with 80% allocated for training data and 20% for test data. The research findings indicate that the testing accuracy levels yielded a value of 85%. The value is derived using the RBF kernel with a gamma value of 0.1 and a C value 10. The Support Vector Machine model is implemented within the Graphical User Interface (GUI). The user experience questionnaire was assessed on the Graphical User Interface, resulting in a user experience score within the "good" category.
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