Identifikasi Tingkatan Ujaran Kebencian Menggunakan Bidirectional Long Short-Term Memory

• M. IQBAL ALIFUDIN
• 14210222

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ABSTRAK

ABSTRAK

Nama              : M. Iqbal Alifudin

NIM                 : 14210222

Program Studi : Ilmu Komputer

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul Tesis  : Identifikasi Tingkatan Ujaran Kebencian Menggunakan Bidirectional Long Short-Term Memory

Peningkatan penggunaan media sosial sebagai sarana interaksi dan berbagi informasi membawa dampak positif dan negatif. Salah satu dampak negatif yang sering muncul adalah ujaran kebencian di media sosial. Ujaran kebencian dapat merugikan individu dan masyarakat serta mengganggu lingkungan online. Oleh karena itu, deteksi tingkat ujaran kebencian menjadi isu yang penting dalam pengelolaan media sosial. Dalam penelitian ini, pendekatan yang menggabungkan teknik deep learning, yaitu Bidirectional Long Short-Term Memory (BiLSTM), dengan berbagai metode pengoptimalan diusulkan. Tujuan utama penelitian ini adalah untuk mencari kombinasi optimizer dan learning rate terbaik yang dapat meningkatkan performa klasifikasi tingkat ujaran kebencian di media sosial. Eksperimen dilakukan dengan menggunakan dataset ujaran kebencian dalam media sosial Twitter. Hasil eksperimen menunjukkan bahwa penggunaan optimasi Adam dengan learning rate ???? menghasilkan akurasi tertinggi sebesar 84.53% dan F1- Score sebesar 0.8459. Hal ini menunjukkan bahwa metode BiLSTM yang di optimasi dengan Adam mampu secara efektif mengatasi permasalahan klasifikasi ujaran kebencian di media sosial.

KATA KUNCI

Tingkatan Ujaran Kebencian,BiLSTM,Optimizer

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DAFTAR PUSTAKA

DAFTAR REFERENSI

[1] M. O. Ibrohim and I. Budi, “A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media,” Procedia Comput Sci, vol. 135, pp. 222–229, 2018, doi: https://doi.org/10.1016/j.procs.2018.08.169.

[2] M. O. Ibrohim, M. A. Setiadi, and I. Budi, “Identification of Hate Speech and Abusive Language on Indonesian Twitter Using the Word2vec, Part of Speech and Emoji Features,” in Proceedings of the International Conference on Advanced Information Science and System, in AISS ’19. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3373477.3373495.

[3] S. D. A. Putri, M. O. Ibrohim, and I. Budi, “Abusive Language and Hate Speech Detection for Indonesian-Local Language in Social Media Text,” in Recent Advances in Information and Communication Technology 2021, P. Meesad, Dr. S. Sodsee, W. Jitsakul, and S. Tangwannawit, Eds., Cham: Springer International Publishing, 2021, pp. 88–98.

[4] I. Duwi Meidha Sari et al., “Pemberdayaan Perempuan Pesisir Dalam Mewujudkan Kampung Krumi Untuk Meningkatkan Perekonomian Desa Bendar,” Pengabdian Kepada Masyarakat, vol. 3, no. 1, 2019.

[5] G. K. Pitsilis, H. Ramampiaro, and H. Langseth, “Detecting Offensive Language in Tweets Using Deep Learning,” Jan. 2018, doi: 10.1007/s10489- 018-1242-y.

[6] M. O. Ibrohim and I. Budi, “A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media,” Procedia Comput Sci, vol. 52 135, pp. 222–229, 2018, doi: https://doi.org/10.1016/j.procs.2018.08.169.

[7] M. O. Ibrohim and I. Budi, “Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter,” in Proceedings of the Third Workshop on Abusive Language Online, 2019, pp. 46–57. doi: 10.18653/v1/W19-3506.

[8] M. E. Sánchez-Gutiérrez and P. P. González-Pérez, “Discriminative neural network pruning in a multiclass environment: A case study in spoken emotion recognition,” Speech Commun, vol. 120, pp. 20–30, Jun. 2020, doi: 10.1016/j.specom.2020.03.006.

[9] A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 2, p. 169, Apr. 2020, doi: 10.22146/ijccs.51743.

[10] S. Kurniawan and I. Budi, “Indonesian tweets hate speech target classification using machine learning,” in 2020 5th International Conference on Informatics and Computing, ICIC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. doi: 10.1109/ICIC50835.2020.9288515.

[11] Y. D. Pramudita, S. S. Putro, and N. Makhmud, “Klasifikasi Berita Olahraga Menggunakan Metode Naïve Bayes dengan Enhanced Confix Stripping Stemmer,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 3, p. 269, Aug. 2018, doi: 10.25126/jtiik.201853810.

[12] M. O. Ibrohim and I. Budi, “Translated vs Non-Translated Method for Multilingual Hate Speech Identification in Twitter,” Int J Adv Sci Eng Inf Technol, vol. 9, no. 4, 2019, doi: 10.18517/ijaseit.9.4.8123. 53

[13] E. Y. Boateng and D. A. Abaye, “A Review of the Logistic Regression Model with Emphasis on Medical Research,” Journal of Data Analysis and Information Processing, vol. 07, no. 04, pp. 190–207, 2019, doi: 10.4236/jdaip.2019.74012.

[14] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Mar. 2016. doi: 10.1145/2939672.2939785.

[15] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput Electron Agric, vol. 147, pp. 70–90, Apr. 2018, doi: 10.1016/j.compag.2018.02.016.

[16] R. J. S. Raj, S. J. Shobana, I. V. Pustokhina, D. A. Pustokhin, D. Gupta, and K. Shankar, “Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things,” IEEE Access, vol. 8, pp. 58006–58017, 2020, doi: 10.1109/ACCESS.2020.2981337.

[17] colah’s blog, “Understanding LSTM Networks,” colah.github.io.

[18] A. A. Rizal and S. Soraya, “Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 18, no. 1, pp. 115–124, Nov. 2018, doi: 10.30812/matrik.v18i1.344.

[19] S. Cornegruta, R. Bakewell, S. Withey, and G. Montana, “Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks,” in Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis, Sep. 2016. doi: 10.18653/v1/W16- 54 6103.

[20] D. Soydaner, “A Comparison of Optimization Algorithms for Deep Learning,” Jul. 2020, doi: 10.1142/S0218001420520138.

[21] I. Z. M. P. Thariq and F. B. Arif, “Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia,” Jurnal Ilmu Siber dan Teknologi Digital (JISTED), vol. 1, no. 1, pp. 1–28, 2022, doi: 10.35912/jisted.v1i1.1509.

[22] A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” Neural and Evolutionary Computing, Mar. 2018, doi: 10.48550/arXiv.1803.08375.

[23] A. D. Gavrilov, A. Jordache, M. Vasdani, and J. Deng, “Preventing Model Overfitting and Underfitting in Convolutional Neural Networks,” International Journal of Software Science and Computational Intelligence, vol. 10, no. 4, pp. 19–28, Oct. 2018, doi: 10.4018/IJSSCI.2018100102.

[24] A. T. Wibowo and M. R. R. Allaam, “Klasifikasi Genus Tanaman Anggrek Menggunakan Metode Convolutional Neural Network (CNN),” in eProceedings of Engineering, 2021.

[25] G. Kang, J. Li, and D. Tao, “Shakeout: A New Approach to Regularized Deep Neural Network Training,” IEEE Trans Pattern Anal Mach Intell, Apr. 2018.

[26] I. Salehin and D. K. Kang, “A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain,” Electronics (Switzerland), vol. 12, no. 14. Multidisciplinary Digital Publishing Institute (MDPI), Jul. 01, 2023. doi: 55 10.3390/electronics12143106.

[27] G. Coppersmith, R. Leary, P. Crutchley, and A. Fine, “Natural Language Processing of Social Media as Screening for Suicide Risk,” Biomed Inform Insights, vol. 10, p. 117822261879286, Jan. 2018, doi: 10.1177/1178222618792860.

[28] A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of Tricks for Efficient Text Classification,” arXiv preprint, Jul. 2016, [Online]. Available: http://arxiv.org/abs/1607.01759

[29] A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of Tricks for Efficient Text Classification,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain: Association for Computational Linguistics, Apr. 2017, pp. 427–431. doi: 10.48550/arXiv.1607.01759.

[30] A. Indrawati, H. Subagyo, A. Sihombing, W. Wagiyah, and S. Afandi, “Analyzing The Impact Of Resampling Method For Imbalanced Data Text In Indonesian Scientific Articles Categorization,” Jurnal Dokumentasi dan Informasi, vol. 41, no. 2, p. 133, Dec. 2020, doi: 10.14203/j.baca.v41i2.702.

[31] R. Padilla, S. L. Netto, and E. A. B. da Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE, Jul. 2020, pp. 237–242. doi: 10.1109/IWSSIP48289.2020.9145130.

[32] A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, Jan. 2021, doi: 10.1016/j.aci.2018.08.003. 56

[33] K. Nugroho, E. Tjahjaningsih, L. Liana, R. Mohamad, and H. Bhakti, “Prediksi Ujaran Kebencian Berbasis Text Pada Sosial Media Menggunakan Metode Neural Network Prediction of Text Based Hate Speech on Socials Media Using the Naural Network,” Jurnal Ilmiah Intech: Information Technology Journal of UMUS, vol. 5, no. 01, pp. 60–68, 2023.

[34] N. I. Pratiwi, I. Budi, and I. Alfina, “Hate speech detection on Indonesian Instagram comments using fasttext approach,” in 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE, 2018. doi: 10.1109/ICACSIS.2018.8618182.

[35] Z. Adhari and Y. Sibaroni, “Identifikasi Ujaran Kebencian pada Twitter Menggunakan Metode Convolutional Neural Network (CNN),” in eProceedings of Engineering, 2023.

[36] M. O. Ibrohim, E. Sazany, and I. Budi, “Identify abusive and offensive language in indonesian twitter using deep learning approach,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Apr. 2019. doi: 10.1088/1742-6596/1196/1/012041.

[37] M. A. Fauzi and A. Yuniarti, “Ensemble method for indonesian twitter hate speech detection,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, no. 1, pp. 294–299, Jul. 2018, doi: 10.11591/ijeecs.v11.i1.pp294-299.

[38] F. W. Wibowo and Institute of Electrical and Electronics Engineers, “Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility,” in 2018 International Conference on Information and Communications Technology (ICOIACT), 2018. doi: 57 10.1109/ICOIACT.2018.8350758.

[39] I. M. A. Niam, B. Irawan, C. Setianingsih, and B. P. Putra, “Hate Speech Detection Using Latent Semantic Analysis (LSA) Method Based On Image,” in 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), 2018. doi: 10.1109/ICCEREC.2018.8712111.

[40] R. Hendrawan and S. Al Faraby, “Multilabel Classification of Hate Speech and Abusive Words on Indonesian Twitter Social Media,” in 2020 International Conference on Data Science and Its Applications (ICoDSA), 2020. doi: 10.1109/ICoDSA50139.2020.9212962.

[41] M. Ridwan and A. Muzakir, “Model Klasifikasi Ujaran Kebencian pada Data Twitter dengan Menggunakan CNN-LSTM,” Jurnal Teknologi dan Informatika, vol. 12, no. 02, pp. 1–5, 2022.

[42] I. Alfina, R. Mulia, M. I. Fanany, and Y. Ekanata, “Hate speech detection in the Indonesian language: A dataset and preliminary study,” in 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE, Oct. 2017, pp. 233–238.

[43] T. T. A. Putri, “ Analisis dan deteksi hate speech pada sosial twitter berbahasa indonesia.,” Master’s thesis, Faculty of Computer Science, Uni versitas Indonesia., 2018.

[44] B. Hakim, “Analisa Sentimen Data Text Preprocessing Pada Data Mining Dengan Menggunakan Machine Learning,” JBASE - Journal of Business and Audit Information Systems, vol. 4, no. 2, Aug. 2021, doi: 10.30813/jbase.v4i2.3000.