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.
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G. R., M. G., and D. M. S. Anbarasi, “Detection and Classification of Cyberbullying Using CR*,” Int. J. Sci. Technol. Eng., vol. 11, no. 4, pp. 24–29, pp. 24-29, Apr. 2023, doi: 10.22214/ijraset.2023.49984.
K. Rong, X.-W. Chu, and Y. Zhao, “Qualitative analyses on the classification model of bystander behavior in cyberbullying,” Frontiers in psychology, vol. 14, p. 1152331, Jul. 2023, doi: 10.3389/fpsyg.2023.1152331.
S. Perumal, “A Survey on Cyberbullying Classification and Detection,” J. Inf. Technol. Digit. World, vol. 5, no. 2, pp. 85–92, 2023, doi: 10.36548/jitdw.2023.2.001.
M. Alauthman, S. R. Yonbawi, and A. Almomani, “Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning,” Computers, Materials & Continua, vol. 75, no. 3, pp. 5307–5319, 2023, doi: 10.32604/cmc.2023.031848.
N. Jahan and R. H. Nabil, “Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition,” I.J. Intelligent Systems and Applications, vol. 15, no. 2, pp. 1–13, Apr. 2023, doi: 10.5815/ijisa.2023.02.01
D. H. and M. Manimaran., “A Review of Machine Learning and AI-Based Approaches to Detecting Cyberbullying on Social Media,” Int. J. Sci. Technol. Eng., vol. 11, no. 4, pp. 1594–1602, Apr. 2023, doi: 10.22214/ijraset.2023.50438.
S. D. and R. K. Dandu, “Machine Learning and Deep Learning Algorithm for Online Bullying Identification,” Int. J. Sci. Technol. Eng., vol. 11, no. 6, pp. 2708–2711, Jun. 2023, doi: 10.22214/ijraset.2023.53951.
Abinaya, K., Jayakumar, D., & Sneha, S, “Bi-LSTM Neural Network Approach to Detect and Recognize Cyberthreats, Cyberstalking and Extremist Tweets in Twitter,” in 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), May. 2023, pp. 1286–1290, doi: 10.1109/ICAAIC56838.2023.10140281.
S. A. Kahate and A. D. Raut, “Design of a Deep Learning Model for Cyberbullying and Cyberstalking Attack Mitigation via Online Social Media Analysis,” in 2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT), pp. 1–7, Feb. 2023, doi: 0.1109/ICITIIT57246.2023.10068711
M. A. Arsha and D. K. Daniel, “Cyberbullying Detection on Social Networks using LSTM Model,” in 2022 International Conference on Innovations in Science and Technology for Sustainable Development (ICISTSD), pp. 293–296, Aug. 2022, doi: 10.1109/ICISTSD55159.2022.10010559
M. Purba, E. Ermatita, A. Abdiansah, V. Ayumi, H. Noprisson, and A. Ratnasari, “A Systematic Literature Review of Knowledge Sharing Practices in Academic Institutions,” in 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS, pp. 337–342, Oct. 2021, doi: 10.1109/ICIMCIS53775.2021.9699350.
M. Purba et al., “Effect of Random Splitting and Cross Validation for Indonesian Opinion Mining using Machine Learning Approach,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 9, 2022, doi: 10.14569/IJACSA.2022.0130917
M. T. Hasan, M. A. E. Hossain, M. S. H. Mukta, A. Akter, M. Ahmed, and S. Islam, “A review on deep-learning-based cyberbullying detection,” Futur. Internet, vol. 15, no. 5, p. 179, 2023, doi: 10.3390/fi15050179.
M. Arif, “A systematic review of machine learning algorithms in cyberbullying detection: future directions and challenges,” J. Inf. Secur. Cybercrimes Res., vol. 4, no. 1, pp. 1–26, Jun. 2021, doi: 10.26735/GBTV9013.
M. R. Ilham and A. D. Laksito, “Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification,” J. Ris. Inform., vol. 5, no. 2, pp. 195–202, Mar. 2023, doi: 10.34288/jri.v5i2.208.
P. Li, Y. Liu, Y. Hu, Y. Zhang, X. Hu, and K. Yu, “A Drift-Sensitive Distributed LSTM Method for Short Text Stream Classification,” IEEE Trans. Big Data, vol. 9, pp. 341–357, Apr. 2023, doi: 10.1109/TBDATA.2022.3164239.
V. Ayumi and I. Nurhaida, “Prediction Using Markov for Determining Location of Human Mobility,” Int. J. Inf. Sci. Technol. – iJIST, vol. 4, no. 1, pp. 1–6, Feb. 2020, doi: dx.doi.org/10.57675/IMIST.PRSM/ijist-v4i1.141.
M. Sadikin and A. Fauzan, “Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset,” Eur. J. Electr. Eng. Comput. Sci., vol. 7, no. 6, pp. 58–64, Dec. 2023, doi: 10.24018/ejece.2023.7.6.583.
H. Noprisson, “Evaluation of Information System Implementation Support for 6-Area Smart City Development,” JSAI (Journal Sci. Appl. Informatics), vol. 6, no. 1, pp. 83–88, Jan. 2023, doi: 10.36085/jsai.v6i1.6087
W. Yang, C. Jia, and R. Liu, “Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM,” Secur. Commun. Networks, vol. 2022, pp. 1–9, 2022, doi: 10.1155/2022/4767980.
X. Xie, M. Xie, A. J. Moshayedi, and M. H. N. Skandari, “A Hybrid Improved Neural Networks Algorithm Based on L2 and Dropout Regularization,” Math. Probl. Eng., vol. 2022, pp. 1–19, 2022, doi: 10.1155/2022/8220453.
B. Pansambal, “Integrating Dropout Regularization Technique at Different Layers to Improve the Performance of Neural Networks,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 4, pp. 716-722, 2023, doi: 10.14569/IJACSA.2023.0140478.
H. Noprisson, E. Ermatita, A. Abdiansah, V. Ayumi, M. Purba, and H. Setiawan, “Fine-Tuning Transfer Learning Model in Woven Fabric Pattern Classification,” Int. J. Innov. Comput. Inf. Control, vol. 18, no. 06, p. 1885, 2022, doi: 10.24507/ijicic.18.06.1885.
D. Ramayanti et al., “Tuberculosis Ontology Generation and Enrichment Based Text Mining,” in 2020 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 429–434, 2020, doi: 10.1109/ICITSI50517.2020.9264922.
Y. Jumaryadi, D. Firdaus, B. Priambodo, and Z. P. Putra, “Determining the Best Graduation Using Fuzzy AHP,” in 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP), pp. 59–63, 2020, doi: 10.1109/BCWSP50066.2020.9249463.
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