MODEL OF INDONESIAN CYBERBULLYING TEXT DETECTION USING MODIFIED LONG SHORT-TERM MEMORY

Abstract

Cyberbullying, in its essence, refers to the deliberate act of exploiting technological tools to inflict harm upon others. Typically, this offensive conduct is perpetuated repeatedly, as the perpetrator takes solace in concealing their true identity, thereby avoiding direct exposure to the victim's reactions. It is worth noting that the actions of the cyberbully and the responses of the individual being cyberbullied share an undeniable interconnection. The main objective of this study was to identify and analyze Instagram comments that contain bullying words using a model of WLSTML2 which is an optimization of a long short-term memory network with word-embedding and L2 regularization. This experiment using dataset with negative labels as many as 400 data and positive as many as 400 data. In this study, a comparison of 70% training data and 30% testing data was used. Based on experimental results, the WLSTMDR model obtained 100% accuracy at the training stage and 80% accuracy at the testing stage. The WLSTML2 model received an accuracy of 99.25% at the training stage and an accuracy of 83% at the testing stage. The WLSTML1 model obtained an accuracy of 97.01% at the training stage and an accuracy of 80% at the testing stage. Based on the experimental results, the WLSTML2 model gets the best accuracy at the training and testing stages. At the testing stage of 132 data, it was found that the positive label data predicted to be correct was 56 data and the negative label data that was predicted to be correct was 53 data