Model Penerapan Sistem Rekomendasi Kuliner Pada Objek Wisata Berbasis User-Based Collaborative Filtering
DOI:
https://doi.org/10.32877/bt.v7i2.1926
Keywords:
Kuliner, Objek Wisata, RecommederNet, Sistem Rekomendasi, User-based Filtering
Abstract
Industri kuliner memiliki peran strategis dalam mendukung perkembangan sektor pariwisata. Namun, meskipun potensi sektor ini sangat besar, pengunjung seringkali menghadapi tantangan dalam memilih pengalaman kuliner yang sesuai dengan preferensi mereka. Hal ini disebabkan oleh kurangnya personalisasi dalam sistem rekomendasi yang ditawarkan. Akibatnya, pengunjung kerap mengalami ketidakpuasan, dan pengelola sektor kuliner kehilangan peluang untuk meningkatkan kepuasan dan loyalitas pelanggan. Penelitian ini bertujuan untuk mengembangkan model rekomendasi kuliner berbasis user-based filtering dan RecommenderNet guna memberikan rekomendasi yang lebih sesuai dengan preferensi individual pengunjung. Penelitian ini menggunakan dataset yang terdiri dari 3.200 data interaksi antara pengunjung dan delapan jenis kuliner di objek wisata Asia Heritage. Data tersebut dianalisis untuk membangun model rekomendasi yang mampu mempelajari pola preferensi pengguna. Model user-based filtering digunakan untuk mengidentifikasi kesamaan antara pengguna berdasarkan riwayat interaksi mereka, sementara RecommenderNet, yang berbasis jaringan saraf tiruan, digunakan untuk meningkatkan akurasi rekomendasi. Hasil penelitian menunjukkan bahwa model yang dikembangkan mampu memberikan rekomendasi dengan tingkat akurasi sebesar 93,63% dan rata-rata tingkat kesalahan (error) sebesar 6,36%. Hasil ini menunjukkan bahwa pendekatan berbasis machine learning dapat secara efektif meningkatkan kualitas rekomendasi kuliner. Dengan implementasi model ini, pengunjung dapat menikmati pengalaman kuliner yang lebih personal dan memuaskan, sementara pengelola objek wisata dapat meningkatkan daya tarik dan kepuasan pelanggan.
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