PENGEMBANGAN SISTEM REKOMENDASI PAKAN KUCING DI GUMILANG PET SHOP BERBASIS WEB MENGGUNAKAN METODE COLLABORATIVE FILTERING BERBASIS APPSHEET

Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi pakan kucing di Gumilang Pet Shop menggunakan metode Collaborative Filtering. Permasalahan yang melatarbelakangi penelitian ini adalah kesulitan konsumen dalam memilih pakan kucing yang sesuai dengan preferensi dan kebutuhan kucing mereka, serta keterbatasan pengetahuan tentang berbagai produk pakan kucing yang tersedia di pasaran. Metode Collaborative Filtering dipilih karena kemampuannya dalam memberikan rekomendasi berdasarkan pola perilaku dan preferensi pengguna yang memiliki kemiripan. Dalam implementasinya, sistem menggunakan pendekatan User-Based Collaborative Filtering dan Item-Based Collaborative Filtering, di mana rekomendasi dihasilkan dengan menganalisis rating dan ulasan yang diberikan oleh pengguna terhadap produk pakan kucing. Sistem mengidentifikasi kesamaan preferensi antar pengguna menggunakan perhitungan similarity dengan rumus Pearson Correlation. Sistem rekomendasi yang dikembangkan berhasil membantu pelanggan dalam menemukan pakan kucing yang sesuai, Penelitian ini memberikan kontribusi praktis dalam pengembangan sistem rekomendasi untuk industri pet shop dan kontribusi teoritis dalam penerapan metode Collaborative Filtering.
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This research aims to develop a cat food recommendation system at Gumilang Pet Shop using the Collaborative Filtering method. The problem behind this research is the difficulty of consumers in choosing cat food that suits their cat's preferences and needs, as well as limited knowledge about the various cat food products available on the market. The Collaborative Filtering method was chosen because of its ability to provide recommendations based on similar user behavior patterns and preferences In its implementation, the system uses a User-Based Collaborative Filtering and Item-Based Collaborative Filtering approach, where recommendations are generated by analyzing ratings and reviews given by users of cat food products. The system identifies similar preferences between users using similarity calculations with the Pearson Correlation formula The recommendation system developed was successful in helping customers find suitable cat food. This research provides a practical contribution in the development of a recommendation system for the pet shop industry and a theoretical contribution in the application of the Collaborative Filtering method.