Penerapan Natural Language Processing (NLP) Dalam Sistem Rekomendasi Wisata Alam Indonesia Berbasis  Sentimen Pengunjung

Aulia Hafiz; Martiano Martiano

Authors

Aulia Hafiz Universitas Muhammadiyah Sumatera Utara
Martiano Martiano Universitas Muhammadiyah Sumatera Utara

Keywords:

Natural Language Processing, Analisis Sentimen, TF-IDF, Support Vector Machine, Sistem Rekomendasi

Abstract

Perkembangan teknologi digital telah mengubah cara wisatawan memperoleh informasi sebelum melakukan perjalanan. Kondisi tersebut dapat menyulitkan calon wisatawan dalam memperoleh informasi yang relevan karena harus membaca dan memahami sejumlah besar ulasan secara manual. Penelitian ini bertujuan menerapkan Natural Language Processing (NLP) dalam sistem rekomendasi wisata alam Indonesia berbasis sentimen pengunjung, mengklasifikasikan ulasan ke dalam sentimen positif, negatif, dan netral, serta memanfaatkan hasil klasifikasi sebagai dasar pemeringkatan destinasi wisata. Data penelitian berupa 70 ulasan pengunjung dari 11 destinasi wisata yang diperoleh dari sumber digital. Tahapan pengolahan data meliputi seleksi dan pembersihan data, pelabelan sentimen, case folding, cleaning dan normalisasi, tokenizing, stopword removal, stemming, serta pembobotan Term Frequency-Inverse Document Frequency (TF-IDF). Klasifikasi sentimen dilakukan menggunakan algoritma Support Vector Machine (SVM) dengan kernel linear. Hasil pengujian menggunakan pembagian data 80:20 menunjukkan bahwa model memperoleh accuracy sebesar 85,71%. Sementara itu, hasil validasi silang stratified 4-fold menghasilkan rata-rata accuracy sebesar 91,42% dengan standar deviasi 2,87%. Hasil penelitian menunjukkan bahwa penerapan NLP dengan TF-IDF dan SVM dapat digunakan untuk mengolah ulasan pengunjung dan menghasilkan rekomendasi destinasi berdasarkan kecenderungan sentimen

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