Sistem Question Answering pada Data Kesehatan Menggunakan Model pre-trained BERT
Abstract
Setelah pandemi covid-19, kesehatan menjadi hal
yang harus diperhatikan. Sebagian besar masyarakat
menggunakan search engine sebagai alat untuk mencari
informasi tentang kesehatan. Namun informasi yang
didapatkan berupa query hasil search engine yang masih
umum. Sistem Question Answering adalah sistem yang
memberikan informasi sesuai informasi yang dibutuhkan oleh
pengguna secara spesifik. Pada penelitian ini dibangun sistem
Question Answering menggunakan metode Bidirectional
Encoder Representations from Transformer (BERT). BERT
merupakan sebuah pre-trained model yang menggunakan
arsitektur transformer. BERT dapat menyelesaikan tugas
sistem Question Answering. Dengan pre-trained model, sistem
tidak perlu melakukan training model dari awal. Sistem hanya
perlu menggunakan train model yang telah dibuat oleh orang
lain sesuai tugas yang dibutuhkan untuk menghemat waktu dan
sumber daya. Untuk mengukur performansi, digunakan metode
Exact Match (EM) dan F1-score. Hasil dari penelitian ini skor
terbaik yang didapat yaitu Exact Match 75% dan F1-score
76%.
Kata kunci— question answering, BERT, pre-trained model,
kesehatan
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