Pendeteksian Hate Speech Pada Sosial Media Indonesia Dengan Algoritma Logistic Regression

Setyaningsih, Siti and , Endang Wahyu Pamungkas, S.Kom, M.Kom. (2023) Pendeteksian Hate Speech Pada Sosial Media Indonesia Dengan Algoritma Logistic Regression. Skripsi thesis, Universitas Muhammadiyah Surakarta.

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Abstract

Hate speech is an act of commenting on someone verbally or non-verbally related to religion, race, gender, behavior and sexual orientation. This is usually done individually or in groups depending on the perpetrator's goals. Usually by way of slander, leading to bad opinions on the victim, so as to make the victim feel uncomfortable. Twitter is one of the most frequently used social media platforms to spread hate speech. Even though preventive measures have been taken by removing or blocking content that contains elements of hate speech, this is still not a way out of this problem due to a lack of understanding from the public about hate speech. This research was conducted to detect hate speech that occurs on Indonesian social media with a machine learning approach. In this study testing was carried out with a logistic regression algorithm. The aim is to identify whether a person's comments contain hate speech or not so that people can be wiser when socializing without hurting others and be more careful in using words or sentences when conveying arguments. This study produces an accuracy value of 84%, while in the process of evaluating the logistic regression model, it obtains a recall value of 0.91 or 94% for label 0 and a recall value of 0.74 or 74% for label 1. Precision values obtain a value

Item Type: Thesis (Skripsi)
Uncontrolled Keywords: hate speech, logistic regression, social media
Subjects: H Social Sciences > HE Communications
H Social Sciences > HE Communications > HE4 Social Media
Divisions: Fakultas Ilmu Komunikasi dan Informatika > Teknik Informatika
Depositing User: SITI SETYANINGSIH
Date Deposited: 22 Aug 2023 00:12
Last Modified: 22 Aug 2023 00:12
URI: http://eprints.ums.ac.id/id/eprint/116652

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