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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of New Media Technology KACANEGARA Jurnal Pengabdian pada Masyarakat JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Jurnal Berdaya Mandiri Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Dinamika Informatika (JDI) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal INFOTEL Teknomatika: Jurnal Informatika dan Komputer Intechno Journal : Information Technology Journal
Kusumaningtyas, Kartikadyota
Universitas Jenderal Achmad Yani Yogyakarta
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Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine Kusumaningtyas, Kartikadyota; Habibi, Muhammad; Dwijayanti, Irmma; Sumiyarini, Retno
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.9820

Abstract

Purpose: Social media, particularly Twitter, provides a venue for individuals to share their thoughts. The public's perception of mental illnesses is often debated on Twitter. So yet, no evaluation of community tweets connected to data on mental health conditions has been performed. The purpose of this study is to examine tweets linked to mental illnesses in Indonesia in order to identify the themes of conversation and the polarity trends of these tweets.Design/methodology/approach: To address this issue, the K-Means Clustering algorithm is utilized to aggregate tweet data that is used to find themes of conversation. The emotion polarity value of each cluster result was then determined using the Support Vector Machine (SVM) approach.Findings/results: This study generated five topic clusters based on tweets about mental illness. While sentiment analysis revealed that all clusters had more negative sentiment classes than positive. Cluster 4 and Cluster 5 had the highest number of negative sentiment values. These clusters emphasize the necessity of consulting with psychiatrists and psychologists if people have mental health disorders, as well as financing for mental health disorder treatment through BPJS Kesehatan services.Originality/value/state of the art: The analysis was done in two stages: data grouping to find themes of conversation using K-Means clustering and SVM to look for positive and negative polarity values associated to twitter data about mental illness.
Co-Authors Adri Priadana Agung Satria Panca Alfirna Rizqi Lahitani Alfun Roehatul Jannah Anas Sufi Hasan Manahingati Aris Wahyu Murdiyanto Ayu Dewi Nurullatifah Bara Falah Adikaputra Bayu Saputra, Andika Cahyani, Natalia Dwi Darma, Firman Diatullah Guna Dedy Hariyadi Dewi, Grita Supriyanto Dwijayanti, Irmma Eko Dwi Nugroho Eko Dwi Nugroho Fitriatul Hasanah Irmma Dwijayanti Kharisma Lahitani, Alfirna Rizqi Mahendra Dewantoro, Septian Muhammad Habibi Muhammad Habibi Natalia Dwi Cahyani NINIK TRI HARTANTI Nurul Fatimah Nurul Fatimah Nurullatifah, Ayu Dewi Puji Winar Cahyo Puji Winar Cahyo Putra, Ikbal Rizki Rizqi Lahitani, Alfirna Saputra, Andika Bayu Septian Mahendra Dewantoro Septiyati Purwandari Subektiningsih Subektiningsih Sujono Riyadi Sumiyarini, Retno Ulfi Saidata Aesyi Wulandari, Irma Rofni