Implementasi Algoritma Support Vector Machine dalam Deteksi Depresi Pada Twitter
Abstract
Mental health is an important part of human life. Over time, mental health is getting more attention along with the increasing number of people who experience mental health disorders. For example, in the U.S., 1 in 5 adults has a mental health disorder, with 8% experiencing depression[1]. Social media, one of which is Twitter as a place for opinions and voices, is often a place for people to convey what they feel. Therefore, writings posted on twitter can be an option to detect a person's mental health, namely depression. To classify between writings that have the characteristics of depression and not, the Support Vector Machine method is used. Based on testing on the Support Vector Machine method for depression classification, the highest accuracy value was obtained at 85,6%.
References
[2] “Depression.” https://www.who.int/news-room/fact-sheets/detail/depression (accessed Sep. 26, 2022).
[3] Datareportal, “The Latest Instagram Statistics: Everything You Need to Know — DataReportal – Global Digital Insights,” Kepios, 2022. https://datareportal.com/essential-instagram-stats?utm_source=DataReportal&utm_medium=Country_Article_Hyperlink&utm_campaign=Digital_2022&utm_term=Indonesia&utm_content=Facebook_Stats_Link (accessed Sep. 29, 2022).
[4] P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
[5] N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm,” J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.
[6] R. Kusumaningrum, I. Z. Nisa, R. P. Nawangsari, and A. Wibowo, “Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning,” Int. J. Adv. Intell. Informatics, vol. 7, no. 3, pp. 292–303, 2021, doi: 10.26555/ijain.v7i3.737.
[7] N. Giarsyani, “Komparasi Algoritma Machine Learning dan Deep Learning untuk Named Entity Recognition : Studi Kasus Data Kebencanaan,” Indones. J. Appl. Informatics, vol. 4, no. 2, p. 138, 2020, doi: 10.20961/ijai.v4i2.41317.
[8] H. Al Azies, D. Trishnanti, and E. M. P. H, “Comparison of Kernel Support Vector Machine ( SVM ) in Classification of Human Development Index ( HDI ),” vol. 6, no. 6, 2019.
[9] S. Awasthi, “Seven Most Popular SVM Kernels,” 2020. https://dataaspirant.com/svm-kernels/#t-1608054630727 (accessed Sep. 29, 2022).
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