Perbandingan Algoritma LSTM Untuk Analisis Sentimen Pengguna Twitter Terhadap Layanan Rumah Sakit Saat Pandemi Covid-19
Kata Kunci:
Twitter, LSTM, GRU, SimpleRNN, BiLTSMAbstrak
Analisis sentimen telah menjadi aspek penting dalam memahami pendapat dan emosi masyarakat tentang berbagai isu. Dalam penelitian ini, dilakukan analisis sentimen pada tweet terkait layanan rumah sakit selama pandemi COVID-19 menggunakan model LSTM, BiLSTM, GRU, dan SimpleRNN. Proses pengumpulan data dilakukan menggunakan Twitter API dan menghasilkan 15.093 tweet. Proses preprocessing data meliputi pembersihan data, case folding, tokenisasi, filtering, dan stemming. Dataset dibagi menjadi 80% untuk pelatihan dan 20% untuk pengujian. Hasilnya menunjukkan bahwa model BiLSTM memiliki akurasi tertinggi sebesar 86%, diikuti model GRU dengan akurasi 86%, model LSTM dengan akurasi 85%, dan model SimpleRNN dengan akurasi 75%. Model BiLSTM juga memiliki MCC tertinggi sebesar 71%. Penelitian ini menyimpulkan bahwa model BiLSTM lebih unggul dibandingkan model lain dalam memprediksi sentimen tweet terkait layanan rumah sakit selama pandemi COVID-19. Temuan penelitian ini dapat memiliki implikasi signifikan bagi penyedia layanan kesehatan dalam meningkatkan kualitas layanan dan meningkatkan kepuasan pasien selama pandemi.
Unduhan
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