ANALISIS SISTEM REKOMENDASI MUSIK BERDASARKAN LIRIK DENGAN METODE TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY

Muhyidin, Muhammad Sahel
Hariyanti, IFani
Novianto, Muhammad Fahmi
Apriliani, Anna Alifia
Rohmah, Sophia Nur

SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Vol 8 (2024): STIMA 8.0 : Menuju Kesinambungan : Inovasi dan Adaptasi Teknologi untuk Pembangunan Be

This study develops and evaluates a music recommendation system using content-based filtering, focusing on lyrical analysis. Utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity metrics, the system analyzes a dataset of 6,049 songs to identify thematically related music based on lyrical content. The methodology involves data preprocessing, feature extraction, and the application of a content-based filtering algorithm to compare song attributes. Results indicate the system's ability to generate relevant recommendations, potentially enhancing user experience in music discovery. This research contributes to the field of personalized content delivery systems, offering insights into the effectiveness of lyric-based music recommendation algorithms