Analisis Sentimen Pelanggan Hotel di Purwokerto Menggunakan Metode Random Forest dan TF-IDF (Studi Kasus: Ulasan Pelanggan Pada Situs TRIPADVISOR)
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Abstract
Aplikasi e-tourism di Indonesia sudah banyak diterapkan terutama untuk layanan akomodasi wisata seperti hotel atau penginapan. Salah satu aplikasi e-tourism yang terkenal adalah tripadvisor.co.id. Aplikasi tersebut memudahkan masyarakat memesan hotel secara online karena lebih cepat, praktis dan mudah. Salah satu faktor penting dalam memilih hotel terbaik dengan harga terjangkau ialah pendapat para pelanggan hotel dari ulasan pada kolom komentar dari para pelanggan hotel sebelumnya. Banyaknya data ulasan pelanggan membutuhkan waktu yang lama untuk mengetahui polaritas ulasan positif dan mana ulasan negatif secara manual. Oleh karena itu diperlukan model analisis sentimen yang akurat yang dapat mengklasifikasikan ulasan pelanggan menjadi ulasan positif dan negatif. Pada penelitian ini diusulkan model analisis sentimen pelanggan hotel menggunakan metode Random Forest Classifier dan Term Frequency–Inverse Document Frequency (TF–IDF). Dataset yang digunakan untuk membangun model sentimen analisis adalah data komentar-komentar pelanggan hotel di Purwokerto yang diunduh dari situs tripadvisor.co.id. Pada preprocessing melibatkan proses konversi slangword menjadi kata baku sesuai KBBI, stemming, dan menambahkan kata-kata stopword baru selain stopword dalam library sastrawi. Hasil penelitian menunjukkan akurasi model mencapai akurasi 87,23%. Akan tetapi jika tanpa proses stemming, akurasi model hanya 76,07%.
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