Rekomendasi Lagu Berdasarkan Lirik Lagu Menggunakan Metode N-gram dan Cosine Similarity - Jesika Silviana Situmorang; Putra Pandu Adikara; Dian Eka Ratnawati (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Listening to songs is one of the human activities that is often carried out by humans. Song is an art that has a pitch or sound that has elements of poetry sequences and a combination with one or several combinations of musical instruments. The lyrics in the song usually contain several verses that have their own meaning for the songwriter. The development of songs has now progressed and made music and song lovers increasingly like songs or music. This happens because of the smartphone that allows song enthusiasts to listen to songs online and offline. But the number of songs available makes music lovers have limitations in choosing songs in the music player. This problem requires an innovation that makes it easy to search for songs based on lyrics that suit the user (music lover). This problem can be solved in the form of an information acquisition system. Song recommendation model can automatically select songs based on lyrics, making it easier for users to search for the desired song. The research for this song recommendation model used the N-gram method (unigram bigram and trigram) and cosine similarity. Song lyrics will go through the preprocessing stage then Term Frequency - Inverse Document Frequency (TF-IDF) so that the words in the song lyrics are selected first. The system will issue 10 song recommendations. The results of the evaluation of the best song recommendations use Unigram with a Precision@10 value of 0.656 and a Mean Average Precison (MAP@K) value of 0.82914032.