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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Jurnal Teknik Elektro Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Perfecting a Video Game with Game Metrics CommIT (Communication & Information Technology) Jurnal Infra International Journal of Industrial Research and Applied Engineering Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat
Indar Sugiarto
Petra Christian University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Forward feature selection for toxic speech classification using support vector machine and random forest Agustinus Bimo Gumelar; Astri Yogatama; Derry Pramono Adi; Frismanda Frismanda; Indar Sugiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp717-726

Abstract

This study describes the methods for eliminating irrelevant features in speech data to enhance toxic speech classification accuracy and reduce the complexity of the learning process. Therefore, the wrapper method is introduced to estimate the forward selection technique based on support vector machine (SVM) and random forest (RF) classifier algorithms. Eight main speech features were then extracted with derivatives consisting of 9 statistical sub-features from 72 features in the extraction process. Furthermore, Python is used to implement the classifier algorithm of 2,000 toxic data collected through the world's largest video sharing media, known as YouTube. Conclusively, this experiment shows that after the feature selection process, the classification performance using SVM and RF algorithms increases to an excellent extent. We were able to select 10 speech features out of 72 original feature sets using the forward feature selection method, with 99.5% classification accuracy using RF and 99.2% using SVM.
Co-Authors Agustinus Bimo Gumelar Alexander Setiawan Andreas Prasetyo Andy Susanto Astri Yogatama Cristian Axenie Derry Pramono Adi Djoni Haryadi Setiabudi Eric Tirtana Evert Oneil Felix Pasila Frismanda Frismanda Handy Wicaksono Jehezkiel Hardwin Tandijaya Jörg Conradt Justinus Andjarwirawan Kartika Gunadi Kartika Gunadi Kevin Koesoemo Lauw Lim Un Tung Liliana Liliana Michael Anggreawan Alexander Mohammad Ismail Rahman Mulia Rudy Petrus Santoso Rudy Adipranata Samuel Febrian Tumewu Saravanakumar Natarajan Siti Halimah Baki Steve Furber Sugiarto Wibowo Suryadi Thiang Thiang Tience Debora Valentina Timothy Joy Siswanto Victor Christian Yohanes TDS Yoken Adinata