Analisis Perbandingan Decision Tree, Support Vector Machine, dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor
DOI:
https://doi.org/10.37012/jtik.v9i1.1429Abstract
Jaringan media sosial pada saat ini terus berkembang dan berdampak pada industri perhotelan. Pelanggan dan traveler telah memposting hasil review secara online untuk menunjukkan tingkat kepuasan mereka terhadap hotel dan berbagi pengalaman terkait hotel yang dikunjungi dengan pelanggan lain yang ada di seluruh belahan dunia. Situs web yang bergerak dalam pariwisata dan perhotelan berkembang pesat secara online seperti Trip advisor. Trip advisor merupakan platform penyedia layanan perjalanan dan pemesanan hotel. Penelitian menggunakan teknik analisis sentimen untuk mengkategorikan opini pengguna yang bernilai negatif maupun positif dengan bantuan kecerdasan buatan yaitu Machine Learning. Penelitian ini menguji tiga algoritma Machine Learning, yaitu Decision Tree Classifier, Support Vector Machine (SVM) dan Xgboost Classifier, dalam melakukan analisis sentimen terhadap review hotel di platform Trip advisor. Hasilnya menunjukkan bahwa Xgboost memiliki tingkat keakuratan (accuracy) yang paling tinggi, mencapai 99%, dibandingkan dengan Decision Tree (97%) dan Support Vector Machine (98%). Dengan demikian, Xgboost dianggap sebagai algoritma terbaik untuk melakukan analisis sentimen pada review hotel di Trip advisor.
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