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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Pengabdian Pada Masyarakat Jurnal SOLMA Prosiding Seminar Nasional Teknoka Seling: Journal of PGRA Study Program Jurnal ICT : Information Communication & Technology Infotech: Journal of Technology Information Jurnal Abdimas ADPI Sosial Dan Humaniora JURNAL TEKNIK INFORMATIKA DAN KOMPUTER
Mia Kamayani
Universitas Muhammadiyah Prof. Dr. HAMKA
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal ICT : Information Communication

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Analisis Sentimen Terhadap Kesehatan Mental Selama Pandemi Covid-19 Berdasarkan Algoritma Naïve Bayes dan Deep Learning Rasiyah Shafa Azizah; Mia Kamayani
Jurnal ICT: Information Communication & Technology Vol. 23 No. 1 (2023): JICT-IKMI, Juli 2023
Publisher : LPPM STMIK IKMI Cirebon

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Abstract

The Covid-19 pandemic is an epidemic that poses a threat to both physical and mental health. The mental health disorders that many people experience during the Covid-19 pandemic are depression, stress, and excessive anxiety. Some people use Twitter and other social media to voice their problems to lessen this effect. The goal of this research is to determine how people feel about mental health amid the Covid-19 pandemic on Twitter. The sentiment data will be analyzed based on assessment findings from the testing model using the Naïve Bayes and Deep Learning algorithms using RapidMiner, and it will be divided into groups of positive sentiment and negative sentiment. This research compares the performance of two algorithms to determine which one performs better while analyzing the public's sentiment toward mental health during the Covid-19 pandemic. According to the research findings, the Deep Learning algorithm performed better with accuracy scores of 86,46%, precision scores of 89,54%, and recall scores of 95,10% than the Naive Bayes algorithm compared to accuracy scores of 76,52%, precision scores of 87,97%, and recall scores of 83,66% at analyzing public sentiment towards mental health during the Covid-19 pandemic.
Co-Authors Atiqah Meutia Hilda Dais Isnafirlah Dwi Anugrah Putra Emilia Roza Estu Sinduningrum Firman Noor Hasan Fitri Yatusholihah Imam Safi'i Iqbal Musyaffa Khusniyati Masykuroh Luqman Abdur Rahman Malik Mohammad Reza Saputra Mohammad Suryadi Syarif Nur Aini Puspitasari PH Gunawan Rasiyah Shafa Azizah Roslaini Roslaini Suciana Wijirahayu