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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Kesehatan Mesencephalon JURNAL DIVERSITA Media Ilmu Kesehatan Jurnal Pengabdian Masyarakat Karya Husada (JPMKH) Jurnal Pengabdian Masyarakat Abdimas Madani Jurnal EduHealth Journal of Innovation in Community Empowerment (JICE) Jurnal Indonesia Sehat (JURINSE) Healthy Behavior Journal
Sumiyarini, Retno
Universitas Jenderal Achmad Yani Yogyakarta
Education Electrical & Electronics Engineering Environmental Science Health Professions Immunology & microbiology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary

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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 Agita Zunilatri Kurniastiti Amini Yuliyani Aulia Devi Septiyani Aulia Devi Septiyani Devi Septiyani, Aulia Diaz, Khristina Dwi Purwanti , Fera Dwi Yati Dwi Yati Dwijayanti, Irmma Dwiyati Fadli, Muh Khoironi Fera Dwi Purwanti Ferianto Ferianto, Ferianto Fitri Hastari Hastari, Fitri Hidayati, Rizqi Wahyu Hutasoit, Masta I Made Yase Sanjaya Ida Nursanti Ida Nursanti Ike Wuri Winahyu Sari Irmma Dwijayanti Khristina Dias Utami Kusumaningtyas , Kartikadyota Kusumaningtyas, Kartikadyota Latifah Susilowati M. Rizki Farhan Mayanti , Lusiyani Rahayu Muhammad Erwan Syah Muhammad Habibi Muhammad Habibi Pamungkas, Dewi Retno Rahmah Rahmah Sujono Riyadi Triansyah, Khusnul Fera Yase Sanjaya, I Made Yase Sanjaya, I Made Zunilatri Kurniastiti, Agita