Deteksi Cyberbullying dengan Mesin Pembelajaran Klasifikasi (Supervised Learning): Peluang dan Tantangan
DOI:
https://doi.org/10.25126/jtiik.2022976747Abstrak
Perkembangan teknologi media sosial tidak hanya memberikan kemudahan dalam berkomunikasi antar individu, akan tetapi juga dapat mengancam kehidupan sosial individu seperti tidakan cyberbullying. Bervariasinya pola dan karakteritik cyberbullying mengakibatkan sulitnya proses deteksi cyberbullying, yang dilakukan oleh pelaku cyberbullying. Penelitian deteksi pola dan karakteristik cyberbullying banyak dilakukan dengan berbagai metode, seperti dengan mengimplementasikan Machine Learning, Natural Language Processing (NLP), dan Sentiment Analysis yang memiliki variasi akurasi yang berbeda, dengan keunggulan dan kelemahan dari masing-masing metode. Implementasi Machine Learning untuk deteksi cyberbullying dapat dilakukan dengan berbagai algoritma, seperti algoritma probabilistik (Naïve Bayes) maupun supervised learning (Support Vector Machine, k-Nearest Neighbour, Decission Tree), dan metode lainnya yang hingga saat ini terus dikembangkan dengan berbagai pendekatan untuk meningkatkan akurasi deteksi cyberbullying atau non-cyberbullying. Adapun peluang dan tantangan penelitian deteksi cyberbullying seperti penerapan pada variasi domain bahasa, dan bentuk ekspresi yang dilakukan pada suatu lingkungan atau budaya, yang masih terdapat ruang untuk dikembangkan dan dijelajah secara luas. Pada artikel ini menjabarkan penelitian berikutnya berupa mengimplementasikan metode pembelajaran klasifikasi (Supervised Learning) dengan modifikasi tahapan untuk meningkatkan akurasi klasifikasi.
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