KLASIFIKASI MOOD LIRIK LAGU MENGGUNAKAN METODE TF-IDF DAN SELF ORGANIZING MAPS

• Main Authors: , ROSITA YANUARTI, , Dr.Techn. Khabib Mustofa, S.Si, M.Kom
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/101017/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=57179
• Institution: Universitas Gadjah Mada

Music has a profound effect on its listener�s mood. Song as a part of music, has components that consists of sound (accoustic) and lyrics (words). The accoustic components includes rhythm, timbre, tone, and tempo, while the lyric component includes words. Mood can be expressed not only by accoustic components, but also by lyric component. Song lyrics which express sadness can make the listeners sad and song lyrics which express happiness can make them happy. This research discuss classifications of a group of lyrics into certain mood classes based on the terms in the lyrics using tf-idf and self organizing maps which iis unsupervised learning algorithm. Metrics value tf-idf as the representation of term frequencies is used to measure the relevance of term to those certain mood classes. This research also discuss the accuracy of classification method using confusion matrix method. The result of this research show that classification model of the lyrics that is generated on training phase is divided into 4 mood classes while model accuracy and the result of classifying the lyrics into 4 mood classes (happy, sad, relax, and angry) on testing phase aren�t classified correctly and having a low classifying result compared to the actual data.