Implementasi Metode Bidirectional LSTM Dengan Word Embedding FastText Dalam Analisis Sentimen Ulasan Pengguna Aplikasi Maxim

Hanz Franklyn Bachruddin Wewengkang, Djihad Wungguli, Nisky Imansyah Yahya, Isran K. Hasan, Siti Nurmardia Abdussamad

Abstract


Aplikasi transportasi online kini menjadi bagian penting dalam kehidupan masyarakat Indonesia. Maxim, sebagai salah satu penyedia layanan, perlu memahami persepsi pengguna untuk meningkatkan kualitas layanannya. Penelitian ini menerapkan metode Bidirectional Long Short-Term Memory (BiLSTM) untuk melakukan klasifikasi sentimen terhadap ulasan pengguna aplikasi Maxim di Google Play Store. Untuk memperkuat representasi kata, digunakan word embedding FastText yang mampu menangkap informasi sub-kata secara lebih baik. Data penelitian diperoleh melalui scraping menggunakan package google-play-scraper pada Python. Model BiLSTM yang dilatih dengan konfigurasi hyperparameter optimal berhasil mengklasifikasikan sentimen ulasan secara efektif, dengan hasil accuracy 94%, precision 96%, recall 95%, dan f1-score 95%. Hasil ini menunjukkan bahwa kombinasi BiLSTM dan FastText mampu mendeteksi sentimen positif dan negatif secara akurat dan seimbang, serta relevan untuk mendukung evaluasi kualitas layanan berbasis opini pengguna.

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References


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DOI: https://doi.org/10.18860/jrmm.v4i5.33358

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