Pemodelan Topik Menggunakan Latent Dirichlet Allocation pada Ulasan Aplikasi PeduliLindungi
DOI:
https://doi.org/10.22441/incomtech.v13i1.15572Kata Kunci:
Pemodelan Topik, Latent Dirichlet Allocation, Pedulilindungi, Google Play Store,Abstrak
Pandemi covid-19 yang melanda seluruh dunia termasuk Indonesia, membutuhkan langkah-langkah pencegahan seperti penelusuran (tracing), pelacakan (tracking) dan pemberian peringatan (warning dan fencing). Salah satu langkah pencegahan yang dilakukan Pemerintah adalah melalui Keputusan Menteri Komunikasi dan Informatika Nomor 171 Tahun 2020 tentang Penetapan Aplikasi Pedulilindungi Dalam Rangka Pelaksanaan Surveilans Kesehatan Penanganan Coronavirus Disease 2019 (Covid-19), menetapkan aplikasi Pedulilindungi sebagai aplikasi surveilans kesehatan penanganan COVID-19. Berbagai komentar disampaikan masyarakat terhadap aplikasi pedulilindungi termasuk melalui kolom ulasan pada playstore. Pada penelitian ini akan dilakukan pemodelan topik menggunakan LDA terhadap ulasan masyarakat tentang aplikasi pedulilindungi. Jumlah data yang digunakan pada penelitian ini sebanyak 13.731 data yang didapatkan dengan melakukan scraping pada google play mulai tanggal 15 september s/d 6 desember 2021 menggunakan library google scrapping. Langkah-langkah yang dilakukan dalam penelitian ini adalah preprocessing dataset, melakukan proses word2vec, menghitung nilai coherence dan melakukan pemodelan topik. Berdasarkan perhitungan nilai coherence pembagian jumlah topik yang ideal adalah 5, kemudian setelah diproses menggunakan algoritma LDA kesimpulan dari ke 5 topik tersebut didefinisikan sebagai kendala pendaftaran, sertifikat vaksin, tanggal lahir yang tidak sesuai, kendala membuka aplikasi dan keluhan pengguna aplikasi.
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