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Aspect-Based Sentiment Analysis Pada Aplikasi Pelacakan Kasus Covid-19 (Studi Kasus: Pedulilindungi)
Abstract
Berbagi pengalaman melalui internet dan media sosial dapat menunjukan sikap dan perasaan dalam bentuk umpan balik. Aplikasi publik yang banyak disoroti pada masa wabah corona virus yaitu aplikasi pedulilindungi yang merupakan aplikasi monitoring perkembangan Corona Virus Disease di Indonesia. Salah satu fenomena timbulnya Aspect-based sentiment dalam pada prilaku sentimentil masyarakat terhadap layanan aplikasi pedulilindungi. Penelitian ini bertujuan untuk mengetahui besarnya nilai sentimen pada layanan pedulilindungi dan berfokus pada aspect based sentiment analysis (ABSA) pada domain ulasan aplikasi pemerintah. Analisis terdiri dari user interface, user experience, fungsionalitas dan work scurity. Metode yang digunakan meliputi klasifikasi sentimen dan aspek dengan metode deep learning (CNN,GRU dan TCN). Data primer bersumber dari hasil ulasan aplikasi pedulilindungi dengan teknik scraping pada situs https://www.pedulilindungi.id/. Hasil penelitian menunjukan bahwa terdapat enam aspek klasisifikasi sentimen pada aplikasi pedulilindungi yaitu aplikasi, user interface, user experience, kode OTP, cek sertifikat vaksin, bukti akses layanan. Hasil penelitian juga menunjukan bahwa metode CNN memperoleh nilai skor akurasi terbaik pada klasifikasi sentimen sebesar 98% dan klasifikasi aspek sebesar 97%.
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