Analisis Tren Pertemuan Tatap Muka Terbatas dari Persepsi Warganet pada Twitter Menggunakan Topic Modeling
Abstract
Salah satu dampak dari pandemi COVID-19 adalah pemberlakuan pembelajaran daring. Kegiatan pembelajaran daring banyak mengalami kendala, sehingga menyebabkan tidak tercapainya kompetensi pembelajaran dengan baik. Kemudian pemerintah memberlakukan program Pertemuan Tatap Muka Terbatas (PTMT), dan banyak tanggapan masyarakat terhadap program tersebut. Untuk mengetahui bagaimana persepsi masyarakat terhadap PTMT, kita dapat melakukan analisis terkait PTMT melalui media sosial. Karena media sosial merupakan salah satu media yang paling banyak digunakan oleh masyarakat selama pandemi untuk berkomunikasi, menyampaikan pendapat, mencari berita, dan lain-lain. Adapun analisis tren PTMT dari persepsi warganet yang dapat dilakukan adalah malalui topic modeling. Dengan mengambil data dari Twitter terkait PTMT menggunakan API (Application Programming Interface), selanjutnya memproses topic modeling dengan metode Latent Dirichlet Allocation (LDA), sebuah metode yang paling populer dan banyak digunakan pada penelitian text mining. Hasil pemodelan topik menghasilkan sepuluh klaster topik dengan nilai koheren 0,50344, dengan tiga topik yang paling banyak dibicarakan oleh warganet. Yakni tentang banyaknya tugas yang harus dikerjakan di luar jam sekolah, mereka sudah mencapai titik kebosanan menjalani sekolah dari rumah hingga menyebabkan kemalasan, dan banyak warganet yang membicarakan bahwa mereka akan melaksanakan kegiatan PTMT.
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