LDA-Topic Modeling: Menggunakan Ulasan Pengguna Untuk Meningkatkan User Experience (Studi pada PeduliLindungi)

Khansa Amaradiena, Tri Widarmanti

Abstract


Pemanfaatan aplikasi tracking COVID-19 PeduliLindungi sebagai salah satu upaya untuk menekan angka penyebaran virus semakin banyak digunakan. Dengan semakin banyak masyarakat yang menggunakan, maka muncul ulasan terkait pemanfaatan aplikasi pada media sosial, salah satunya Twitter. Penelitian bertujuan untuk mengetahui topik yang disebutkan dan topik yang paling sering dibicarakan oleh pengguna aplikasi PeduliLindungi berdasarkan data pada media sosial Twitter. Selain itu, tujuan lain dari penelitian ini untuk mengetahui topik muncul yang berkaitan dengan user experience. Topik terkait user experience tersebut kemudian dikelompokan berdasarkan UX HEART metrics. Penelitian menggunakan data UGC dari tweets pada Twitter dengan kata kunci “pedulilindungi”. Data diambil dalam rentan waktu 1 – 31 Desember 2021. Data kemudian dianalisis menggunakan metode LDA-Topic Modeling untuk mengetahui topik dan kata pada dokumen. Hasil penelitian menunjukan terdapat 9 topik yang disebutkan. Selain itu, topik yang paling sering dibicarakan terkait penggunaan aplikasi PeduliLindungi. Berdasarkan analisis UX HEART metrics dapat diketahui terdapat 4 metrics yaitu happiness, engagement, retention, dan task success. Diharapkan dari hasil penelitian dapat menjadi evaluasi bagi pengembang aplikasi untuk meningkatkan kualitas aplikasi, seperti mengurangi error, melakukan pembaharuan data vaksin secara cepat, dan menyebarkan informasi untuk pengguna tentang cara melakukan input tanggal saat log in atau saat melakukan klaim sertifikat vaksin.
Kata Kunci: Ulasan Pengguna, Text Mining, Topic Modeling, Pengalaman Pengguna.

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References


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DOI: https://doi.org/10.37531/sejaman.v6i1.4227

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