PERBANDINGAN ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI TINGKAT DEPRESI, KECEMASAN, DAN STRES - Jamie Saviola (2023)
Mental health is just as important as physical health. With a healthy mentality, everyone is able to make decisions and live a good social life. Poor mental health can encourage people to have negative thoughts and if it's left too long, it can affect physical health as well. Detecting mental health problems as soon as possible is important to determine the appropriate treatment to prevent mental disorder problems. If it has been diagnosed as a mental disorder, then the treatment and care carried out will be more complicated and take a longer time. Therefore, this study will apply the Naive Bayes and Support Vector Machine algorithms to predict the level of depression, anxiety, and stress based on a person's background. The purpose of this study is to find out how the model's performance on classification and how much a person's background affects depression, anxiety, and stress levels. The dataset used in this study is the result of answers to questionnaires distributed to student studying in jabodetabek. The result of this study indicate that the Naïve Bayes model has better accuracy that the Support Vector Machine model in classifying levels of depression, anxiety, and stress. Where the accuracy value on the depression scale of Naïve Bayes model is 63% and the Support Vector Machine is 55%. On the anxiety scale, the Naïve Bayes model is 72% and the Support Vector Machine is 69%. On the stress scale, the Naïve Bayes model is 59% and the Support Vector Machine is 46%.