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All Journal Jurnal Media Infotama Pixel : Jurnal Ilmiah Komputer Grafis JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Pseudocode Jurnal Informatika Upgris JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak JSAI (Journal Scientific and Applied Informatics) Journal of Technopreneurship and Information System (JTIS) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JISA (Jurnal Informatika dan Sains) Journal of Applied Data Sciences Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat ABDIMAS Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Sistem Informasi Jurnal Ilmu Komputer Dan Informatika eProceedings of Engineering Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Informatika: Jurnal Pengembangan IT Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Applied Information Technology and Computer Science (AICOMS)
Dedy Agung Prabowo
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Journal : JISA (Jurnal Informatika dan Sains)

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The Classification of Anxiety, Depression, and Stress on Facebook Users Using the Support Vector Machine Tsania Maulidia Wijiasih; Rona Nisa Sofia Amriza; Dedy Agung Prabowo
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1273

Abstract

Social media remains an essential platform for connecting people with friends, family, and the world around them. However, when events spread on social media are primarily negative, it will cause depression, anxiety, and stress that tend to increase. This study aims to classify depression, anxiety, and stress using the Support Vector Machine. The data in this study were obtained from active Facebook users using the Depression Anxiety Stress Scale (DASS 21) questionnaire. This study adopted the Knowledge Discover Database process. The result of this study is an evaluation of the performance of the Support Vector Machine classification of depression, anxiety, and stress. The accuracy of the Support Vector Machine in this study is 98.96%.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Agustomi, Endri Akhdan Syarif Hidayatullah, Dias Akhmad, Fajar Kamaludin Alon Jala Tirta Segara Andri Sarpiadi Ardi Susanto Ardi Susanto Aritonang, Sudarsono Azzahra, Fathya Yuanita Cahyo Prihantoro Dandi Sunardi Dedy Abdullah Dedy Abdullah Dernata, Jaka Dimas Fanny Hebrasianto Permadi Dofiyer, Fernaldo Christofer Eki Agustiawan Fauzian Setiawan, Kelvin Fernaldo Christofer Dofiyer Firdaus, Ammar Musthofa Gunawan Gunawan Hengki Putra Irawan Jaka Dernata Kirman Kirman, Kirman M Yoka Fathoni marhalim, marhalim Marsally , Silvia Van Marsally, Silvia Van Muflih Haura Muhamad Azrino Gustalika Muhammad Husni Rifqo Muna, Bunga Laelatul Nicolaus Euclides Wahyu Nugroho Novian Adi Prasetyo Paradise Perdi Leo Ade Candra Pratama, Rendra Agung Putra, Erwin Dwika Rachman, Ari Rakhma, Nazwa Aulia Ramdani, Cepi Rendra Agung Pratama Rendra Agung Pratama Resad Setyadi Rifqo, Muhammad Husni Rona Nisa Sofia Amriza Sandhy Fernandes Sandhy Fernandez Sarah Astiti S.Kom., M.MT Silvia Van Marsally Sonita, Anisya Sudianto Sudianto, Sudianto Sundari Sundari Syaputri, Yopita Syarif Hidayatullah, Dias Akhdan Tarigan, Emya Ninta Teguh Ramadhani, Dimas Toyib, Rozali Triaji Morgana Kaban Tsania Maulidia Wijiasih Usman, Muhammad Lulu Latif Utami, Annisaa Wicaksono, Apri Pandu Yuza Reswan