Pemanfaatan Metode Collaborative Filtering dengan Algoritma KNN  pada Sistem Rekomendasi Produk

KURNIA RAMADHAN PUTRA; ILHAM FATHUR RAHMAN

doi:10.26760/mindjournal.v9i1.113-123

Pemanfaatan Metode Collaborative Filtering dengan Algoritma KNN pada Sistem Rekomendasi Produk

KURNIA RAMADHAN PUTRA, ILHAM FATHUR RAHMAN

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Abstrak

Salah satu permasalahan customer pada e-commerce adalah sulitnya menemukan produk yang diinginkan untuk dibeli. Sistem rekomendasi mampu menangani permasalahan tersebut dengan cara mengalisis data profil customer untuk menyaring produk yang sesuai dengan profil customer kemudian merekomendasikannya kepada customer tersebut. Untuk mengetahui hubungan antara produk dengan pengguna maka dapat memanfaatkan sistem rekomendasi. Ada beberapa permasalahan pada sistem rekomendasi yaitu sparsity data, missing value, dan duplikasi data yang sering ditemukan pada data berbasis rating seperti pada e-commerce. Untuk menyelesaikan masalah ini, maka diusulkan metode Item-based Collaborative Filtering dan algoritma K-Nearest Neighbor (KNN) dengan hasil evaluasi nilai MAE sebesar 1,05 dan RMSE sebesar 1,36 yang mampu menangani sistem rekomendasi dengan baik dengan tingkat kesalahan yang kecil.

Kata kunci: recommendation system, item-based collaborative filtering, KNN, Sparsity Data, Cold-Start.

AbstractIn e-commerce, one common customer problem is difficulty in finding the product they want to buy. This issue can be addressed through a recommendation system, which analyzes customer profile data to filter products that match the customer's profile and then recommends them. One way to establish the relationship between products and users is by using a recommendation system. However, recommendation systems often encounter problems such as data sparsity, missing values, and data duplication, particularly in rating-based data. To address these issues, the Item-based Collaborative Filtering method and the K-Nearest Neighbor (KNN) algorithm are proposed. Evaluation results show that these methods have MAE values of 1.05 and RMSE of 1.36, indicating their effectiveness in handling the recommendation system with a low error rate.

Keywords: recommendation system, collaborative filtering, item-based CF, KNN

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Referensi

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DOI: https://doi.org/10.26760/mindjournal.v9i1.113-123