Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu

KURNIA RAMADHAN PUTRA; MOHAMMAD ADITIYA RACHMAN

doi:10.26760/mindjournal.v9i2.179-193

Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu

KURNIA RAMADHAN PUTRA, MOHAMMAD ADITIYA RACHMAN

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Abstrak

Sistem rekomendasi dapat dimanfaatkan untuk membantu pengguna menemukan item atau informasi sesuai preferensi mereka, termasuk lagu. Metode seperti Collaborative Filtering (CF), Content-Based Filtering (CBF), dan Hybrid Filtering digunakan untuk meningkatkan kualitas rekomendasi berdasarkan interaksi pengguna dan karakteristik konten. Penelitian ini membandingkan efektivitas ketiga metode tersebut dalam rekomendasi lagu menggunakan dataset dengan 68.330 entri data. Metode CF dan CBF diterapkan secara terpisah, lalu dikombinasikan dalam pendekatan hybrid untuk mengevaluasi peningkatan hasil. CF mencapai presisi 49.9%, CBF 39.5%, sedangkan hybrid CF-CBF mencatat presisi tertinggi sebesar 50.7%. Sebaliknya, hybrid CBF-CF menghasilkan presisi terendah, yaitu 38.4%. Kesimpulannya, pendekatan hybrid CF-CBF lebih unggul dalam merekomendasikan lagu sesuai preferensi pengguna dibandingkan metode lainnya.

Kata kunci: sistem rekomendasi, rekomendasi lagu, content-based filtering, collaborative filtering, hybrid filtering

Abstract
Recommender systems can be utilized to assist users in discovering items or information that align with their preferences, including music. Methods such as Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering enhance recommendation quality based on user interactions and content characteristics. This study compares the effectiveness of these three methods in music recommendation using a dataset containing 68,330 entries. CF and CBF were implemented separately and combined in a hybrid approach to evaluate performance improvements. CF achieved a precision of 49.9% and CBF 39.5%, while the hybrid CF-CBF approach recorded the highest precision at 50.7%. In contrast, the hybrid CBF-CF approach yielded the lowest precision, at 38.4%. In conclusion, the hybrid CF-CBF approach outperforms other methods in delivering music recommendations tailored to user preferences.

Keywords: recommendation system, song recommendation, content-based filtering, collaborative filtering, hybrid filtering

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Referensi

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DOI: https://doi.org/10.26760/mindjournal.v9i2.179-193