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All Journal Prosiding Seminar Nasional Teknoka Jurnal ICT : Information Communication & Technology
Rasiyah Shafa Azizah
Universitas Muhammadiyah Prof. Dr. Hamka
Computer Science & IT Engineering

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Visualisasi Data Ulasan Pembelajaran Jarak Jauh dan Gangguan Somatoform Terhadap Mahasiswa Fakultas Teknik UHAMKA Menggunakan Software R-Studio Rizka Nisa Aqila; Rasiyah Shafa Azizah; Reza Kurnia Khoirunisa; Fajar Sidik
Prosiding Seminar Nasional Teknoka Vol 6 (2021): Prosiding Seminar Nasional Teknoka ke - 6
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

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Abstract

Pandemi virus corona (Covid-19) memaksa aktivitas belajar mengajar tatap muka dihentikan kemudian dialihkan ke dunia maya yang disebut dengan Pembelajaran Jarak Jauh (PJJ). Kebijakan tersebut telah diterapkan diberbagai institusi pendidikan termasuk Universitas Muhammadiyah Prof. Dr. Hamka (UHAMKA) Jakarta yang mengakibatkan mahasiswa harus beradaptasi dengan metode baru dan berdampak pada munculnya gangguan somatoform. Penelitian dilaksanakan untuk mengetahui ulasan terkait PJJ dan gejala gangguan somatoform yang sering dialami mahasiswa Fakultas Teknik UHAMKA selama masa pandemi Covid-19. Data diperoleh dengan penyebaran kuesioner secara online kepada mahasiswa tahun angkatan 2018 sampai 2020 yang berjumlah 204 mahasiswa aktif Fakultas Teknik UHAMKA. Hasil penelitian ini berupa visualisasi data wordcloud dan matriks yang diolah menggunakan software R-Studio. Berdasarkan hasil visualisasi data yang sudah diolah dapat ditarik kesimpulan bahwa selama masa pandemi mahasiswa Fakultas Teknik UHAMKA mengalami kendala pada ‘materi’, ‘dosen’, ‘belajar’, dan ‘sinyal’, sedangkan untuk gejala gangguan somatoform yang sering dialami oleh mahasiswa adalah sakit kepala.
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 Fajar Sidik Mia Kamayani Reza Kurnia Khoirunisa Rizka Nisa Aqila