Artist Recommendation System Based on Number of Interactions Using Collaborative Filtering Method
Abstract
Sistem rekomendasi adalah suatu sistem penyaringan yang bertujuan untuk memprediksi preferensi yang diberikan oleh pengguna terhadap suatu elemen tertentu, pada penelitian ini terhadap sebuah artis. Penelitian ini memiliki tujuan untuk meningkatkan hasil performansi sistem rekomendasi artis menggunakan metode collaborative filtering. Metode collaborative filtering menggunakan informasi pengguna dan artis dalam membangun rekomendasi. Dataset yang digunakan mencakup jumlah pemutaran lagu oleh pengguna. Metode collaborative filtering diimplementasikan dengan melakukan perhitungan similarity antar pengguna dan antar artis. Perhitungan similarity yang digunakan, menggunakan cosine similarity. Setelah dilakukan perhitungan kesamaan, dilakukan perhitungan weighted sum dan menghasilkan prediksi. Evaluasi performansi dihitung menggunakan MAE (Mean Absolute Error), MSE (Mean Squared Error), dan RMSE (Root Mean Squared Error). Hasil evaluasi yang didapatkan pada penelitian ini adalah MAE 9,474, MSE 52.653,40 dan RMSE 229,208 pada perbandingan 70:30. Sedangkan pada perbandingan 75:25 menghasilkan MAE 9,902, MSE 45.914,85 dan RMSE 210,017. Pada perbandingan 80:20 hasil yang didapatkan adalah MAE 10,486, MSE 48.764,51 dan RMSE 217,416. Hasil tersebut menunjukkan bahwa, semakin besar rasio data train terhadap data test, nilai MAE, MSE dan RMSE cenderung meningkat.
Kata kunci: Sistem Rekomendasi, Collaborative filtering, Cosine similarity, MAE, MSE, RMSE.
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