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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Ultimacomm: Jurnal Ilmu Komunikasi
Astri Yogatama
Petra Christian University
Computer Science & IT Engineering Languange, Linguistic, Communication & Media

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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.
Changes in Nonverbal Communication in Public Communication Before and During the COVID-19 Pandemic: Literature Review of Scientific Papers for the 2014-2022 Period Angela Audrey Sutanto; Astri Yogatama; Liliana .; Hans Juwiantho
Ultimacomm: Jurnal Ilmu Komunikasi Vol 14 No 2 (2022): UltimaComm
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ultimacomm.v14i2.2953

Abstract

Nonverbal communication is all messages or all communication cues that are not words or do not use words. The COVID-19 pandemic, has changed aspects of non-verbal communication, especially in gestures, paralinguistics, and microexpression in the context of public communication. This research aims to find the differences in nonverbal communication, gestures, paralinguistics, and microexpression before and during the COVID-19 pandemic. The data are from 30 journals with the central theme of non-verbal communication in the context of public communication and containing specific classifications of gestures, paralinguistics, and microexpressions. This research uses the quantitative-descriptive content analysis method with. Meanwhile, the theory used in this study is the theory of J. Schneider, D. Börner, P. van Rosmalen & M. Specht (2017) and Vanessa Van Edwards' characteristics of microexpression (2013). Thus the aspects of nonverbal communication that have changed the most from before and during the COVID-19 pandemic are gestures (upright and calm body position) and paralinguistics (tempo), meanwhile microexpession did not experience significant changes.
Co-Authors Agustinus Bimo Gumelar Angela Audrey Sutanto Derry Pramono Adi Frismanda Frismanda Hans Juwiantho Indar Sugiarto Liliana .