Sistem Rekomendasi Kuliner Karanganyar Menggunakan Metode Hybrid Recommendation
Abstract
Karanganyar has many culinary tourist destinations, but the number of diverse choices often makes it difficult for users to choose where to eat according to their preferences. Therefore, this research aims to help users find culinary places based on special menus. The recommendation system will be developed using a hybrid approach by combining content-based filtering and collaborative filtering. The content-based method matches the special menu with user input using cosine similarity, while collaborative filtering calculates the score from the normalized rating and the number of reviews using the weighted sum. A weight of 0.6 is given for content-based and 0.4 for collaborative because direct preference for menus is considered more dominant. The test was carried out using 5 different menus using precision and recall, with the test results for 100% recall, and the precision got a score of 75.42% because there are still recommendations displayed in the system that only have similar characteristics but are not really relevant to the user's preferences
Keywords: Collaborative filtering; Content-based filtering; Hybrid recommendation; Culinary recommendation; Recommendation system.
Abstrak
Karanganyar memiliki banyak destinasi wisata kuliner, namun jumlah pilihan yang beragam sering menyulitkan pengguna dalam menentukan tempat makan sesuai preferensi. Oleh karena itu, penelitian ini bertujuan untuk membantu pengguna menemukan tempat kuliner berdasarkan menu spesial. Sistem rekomendasi yang akan dikembangkan menggunakan pendekatan hybrid dengan menggabungkan content-based filtering dan collaborative filtering. Metode content-based mencocokkan menu spesial dengan input pengguna menggunakan cosine similarity, sedangkan collaborative filtering menghitung skor dari rating yang dinormalisasi dan jumlah ulasan dengan menggunakan weighted sum. Bobot 0,6 diberikan untuk content-based dan 0,4 untuk collaborative karena preferensi langsung terhadap menu dinilai lebih dominan. Pengujian dilakukan dengan menggunakan 5 menu yang berbeda menggunakan precision dan recall, dengan mendapatkan hasil pengujian untuk recall 100%, dan precision mendapatkan nilai 75,42% karena masih ada rekomendasi yang ditampilkan disistem yang hanya mirip karakteristiknya tetapi tidak benar-benar relevan dengan preferensi pengguna
Kata Kunci: Collaborative filtering; Content-based filtering; Hybrid recommendation; Rekomendasi kuliner; Sistem rekomendasi
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