IMPLEMENTASI SMOTE DAN ALGORITMA MACHINE LEARNING UNTUK MENINGKATKAN AKURASI REKOMENDASI HOTEL
DOI:
https://doi.org/10.21927/ijubi.v7i2.5141Keywords:
SMOTE, rekomendasi hotel, algoritma machine learningAbstract
Pariwisata memiliki peran penting dalam perekonomian global, dengan destinasi seperti Candi Borobudur menarik berbagai jenis pengunjung. Untuk meningkatkan pengalaman wisatawan, rekomendasi hotel yang akurat menjadi sangat penting. Namun, data yang tidak seimbang, seperti ulasan positif yang terlalu dominan, sering kali mengurangi kinerja model machine learning yang digunakan untuk rekomendasi. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan Synthetic Minority Over-sampling Technique (SMOTE) guna menyeimbangkan dataset dan meningkatkan akurasi rekomendasi hotel. Beragam algoritma machine learning, termasuk Random Forest, Support Vector Machines, dan Neural Networks, diterapkan dan dievaluasi. Hasil penelitian menunjukkan bahwa penerapan SMOTE secara signifikan meningkatkan kinerja semua model, dengan Random Forest memberikan hasil terbaik. Studi ini menunjukkan bahwa SMOTE, dalam kombinasi dengan teknik machine learning, memberikan solusi yang kuat terhadap ketidakseimbangan kelas pada sistem rekomendasi hotel, sehingga menghasilkan rekomendasi yang lebih andal dan relevan bagi wisatawan. Temuan ini memiliki implikasi penting bagi manajemen hotel dan sektor pariwisata secara keseluruhan.References
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