BibTex Citation Data :
@article{JMASIF34875, author = {Emilia Widiyanti and Sukmawati Endah}, title = {Pengenalan Emosi dalam Musik Berdasarkan Musical Features Menggunakan Support Vector Regression}, journal = {JURNAL MASYARAKAT INFORMATIKA}, volume = {11}, number = {2}, year = {2020}, keywords = {Soundtrack, Music Emotion Recognition, Musical Features, Support Vector Regression}, abstract = {Musik dibuat untuk menyampaikan emosi dan seringkali dimanfaatkan dalam berbagai kegiatan sehari-hari. Music Emotion Recognition atau pengenalan emosi dalam musik menjadi salah satu bidang penelitian yang ikut berkembang seiring dengan perkembangan jenis dan pemanfaatan musik. Penelitian ini menyajikan hasil pengenalan emosi pada musik dengan musical features menggunakan Support Vector Regression dengan jenis pelatihan ɛ-Support Vector Regression dan ʋ-Support Vector Regression serta kombinasi fitur terbaik yang menghasilkan model terbaik. Data yang digunakan sejumlah 165 data musik yang berbentuk musik soundtrack instrumental. Dari penelitan ini dihasilkan dua model terbaik menggunakan pelatihan ʋ-SVR. Model yang dihasilkan yaitu model pengenalan angle dengan masukan fitur terbaik adalah fitur Pitch dan Energy , dan model pengenalan distance dengan masukan fitur terbaik Zero Crossing Rate dan Beat. Model dihasilkan dengan nilai parameter pelatihan model untuk cost=2 7 , gamma=2 -7 dan nu=2 -2 pada model angle dan cost=2 7 , gamma=2 -8 dan nu=2 -2 pada model distance. Pengenalan dengan kedua model tersebut menghasilkan akurasi sebesar 37,75%.}, issn = {2777-0648}, pages = {1--14} doi = {10.14710/jmasif.11.2.34875}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/34875} }
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