Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Liana Shinta Dewi
Fakultas Ilmu Budaya, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Identifikasi Tweet Cyberbullying pada Aplikasi Twitter menggunakan Metode Support Vector Machine (SVM) dan Information Gain (IG) sebagai Seleksi Fitur Ni Made Gita Dwi Purnamasari; Mochammad Ali Fauzi; Indriati Indriati; Liana Shinta Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1030.214 KB)

Abstract

Cyberbullying is one of the actions that violate the ITE Law where the crime is committed on social media applications such as Twitter. This action is difficult to detect if no one is reporting the tweet. Cyberbullying tweet identification aims to classify tweets that contain bullying. Classification is done using Support Vector Machine method where this method aims to find the dividing hyperplane between negative and positive class. This study is a text classification where more data is used, the more features are produced, therefore this research also uses Information Gain as feature selection to select features that are not relevant to the classification. The process of the system starts from text preprocessing with tokenizing, filtering, stemming and term weighting. Then perform the information gain feature selection by calculating the entropy value of each term. After that perform the classification process based on the terms that have been selected, and the output of the system is identification whether the tweet is bullying or not. The result of using SVM method is accuracy 75%, precision 70.27%, recall 86.66% and f-measure 77.61% on experiment iterMax value = 20, λ = 0.5, γ = 0.001, ε = 0.000001, and C = 1. The best threshold of information gain is 90%, with accuracy 76.66%, precision 72.22%, recall 86.66% and f-measure 78.78%
Co-Authors Indriati Indriati M. Ali Fauzi Ni Made Gita Dwi Purnamasari