Analisis Sentimen Terhadap Ulasan Aplikasi Shopee di Google Play Store Menggunakan Metode TF-IDF dan Long Short-Term Memory)
AbstractPengunjung Shopee semakin meningkat dari tahun 2022 hingga 2023. Karena peningkatan itu, semakin banyak pengguna yang berkomentar negatif atau positif. Maka, mengetahui sentimen pengguna pada aplikasi Shopee dapat mengetahui perilaku pelanggan dan meningkatkan penjualan. Penelitian ini menggunakan metode TF-IDF dan algoritma LSTM. Adapun tahapan penelitian seperti scrapping data yang menggunakan ulasan pengguna aplikasi Shopee di Google Play Store sebanyak 3565 data. Lalu data dikategorikan menjadi tiga kelas: positif, netral, dan negatif. Proses preprocessing meliputi Tokenization, Normalization, Stopword, dan Stemming. Selanjutnya dilakukan proses train data dan data test sebesar 8:2. Lalu melakukan vektorisasi dengan TF-IDF, melatih model dengan penggabungan TF-IDF dan LSTM (Long Short-Term Memory), serta menggunakan metrics untuk mengevaluasi model dan visualisasi menggunakan word cloud. menghasilkan akurasi sebesar 83% dengan nilai loss (kerugian) sebesar 0.1385. Model memiliki kemampuan cukup baik dalam memprediksi kelas negatif dan positif tetapi kurang efektif untuk kelas netral karena data yang kurang seimbang.
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