Klasifikasi Depresi Kecemasan Dan Stres Pada Pengguna Media Sosial Facebook Menggunakan Support Vector Machine
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Tsania, Maulidia Wijiasih
Subject
TA Engineering (General). Civil engineering (General)
Datestamp
2022-08-09 08:04:11
Abstract :
Social media remains an important platform for connecting people with friends, family and the world around them. The 2014 Indonesian Family Life Survey (IFLS) surveyed 22,423 individuals in Indonesia, the survey showed that one standard deviation in social media use was a 9% increase in CES-D (Center for Epidemiological Studies Depression Scale) scores. This proves that social media has a negative impact on mental health. When events spread on social media are negative, they will cause depression, anxiety, and stress that tend to increase. This study aims to determine the performance of the Support Vector Machine in classifying depression, anxiety, and stress. The research data was obtained from active Facebook research using the Depression, Anxiety, and Stress Scale (DASS 21) and the problem solving in this study adopted the Knowledge Discovery Database process. The results of the Support Vector Machine research in classifying depression are 98.96% accuracy, anxiety with 98.44% accuracy, and stress with 99.48% accuracy. Keyword : Support Vector Machine, DASS 21, Depression, Anxiety, Stress.
Social media remains an important platform for connecting people with friends, family and the world around them. The 2014 Indonesian Family Life Survey (IFLS) surveyed 22,423 individuals in Indonesia, the survey showed that one standard deviation in social media use was a 9% increase in CES-D (Center for Epidemiological Studies Depression Scale) scores. This proves that social media has a negative impact on mental health. When events spread on social media are negative, they will cause depression, anxiety, and stress that tend to increase. This study aims to determine the performance of the Support Vector Machine in classifying depression, anxiety, and stress. The research data was obtained from active Facebook research using the Depression, Anxiety, and Stress Scale (DASS 21) and the problem solving in this study adopted the Knowledge Discovery Database process. The results of the Support Vector Machine research in classifying depression are 98.96% accuracy, anxiety with 98.44% accuracy, and stress with 99.48% accuracy. Keyword : Support Vector Machine, DASS 21, Depression, Anxiety, Stress.
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