Article Per Year (5 Year)
p-Index From 2019 - 2024
1.431
P-Index
This Author published in this journals
All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Indonesia Symposium on Computing Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Reswara: Jurnal Pengabdian Kepada Masyarakat IJISTECH eProceedings of Engineering
Yanuar Firdaus Arie Wibowo
Telkom University
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Emotion Classification of Song Lyrics using Bidirectional LSTM Method with GloVe Word Representation Weighting Jiddy Abdillah; Ibnu Asror; Yanuar Firdaus Arie Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (602.312 KB) | DOI: 10.29207/resti.v4i4.2156

Abstract

The rapid change of the music market from analog to digital has caused a rapid increase in the amount of music that is spread throughout the world as well because music is easier to make and sell. The amount of music available has changed the way people find music, one of which is based on the emotion of the song. The existence of music emotion recognition and recommendation helps music listeners find songs in accordance with their emotions. Therefore, the classification of emotions is needed to determine the emotions of a song. The emotional classification of a song is largely based on feature extraction and learning from the available data sets. Various learning algorithms have been used to classify song emotions and produce different accuracy. In this study, the Bidirectional Long-short Term Memory (Bi-LSTM) deep learning method with weighting words using GloVe is used to classify the song's emotions using the lyrics of the song. The result shows that the Bi-LSTM model with dropout layer and activity regularization can produce an accuracy of 91.08%. Dropout, activity regularization and learning rate decay parameters can reduce the difference between training loss and validation loss by 0.15.
Co-Authors Adityo Nugroho Amarilis Putri Yanuarifiani Amarilis Putri Yanuarifiani Andi Novia Rilyani Annas Abdillah Marta Citra Rizki Pratiwi Dana Sulistiyo Kusumo Dawam Dwi Jatmiko Suwawi Dwi Kurnia Putri Eko Darwiyanto Hendri Priyanto Ibnu Asror Jesira Enjeliva Br. Girsang Jiddy Abdillah Laksitowening, Kusuma Ayu Mahmud Imrona Nicodemus Nelwan Rahmat Firdaus Siti Rahmah Amaliah Siti Sa'adah Tora Fahrudin Widia Febriyani Widia Febriyani ZK Abdurahman Baizal