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Cyberbullying is an activity of sending threatening messages to insult person. To prevent cyber victimization from the activity is challenging. Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. This project enhanced the Naïve Bayes classifier for extracting the words and examining loaded pattern clustering. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes. Cyberbullying constitutes a threat to adolescents’ psychosocial wellbeing that developed alongside technological progress. Detecting online bullying cases is still an issue because most of victims and bystanders do not timely report cyberbullying episodes to adults. Therefore, automatized technologies may play a critical role in detecting cyberbullying through the use of Machine Learning (ML). ML covers a broad range of techniques that enables systems to quickly access and learn from data, and to make decisions about complex problems. This contribution aims at deepening the role of ML in cyberbullying detection and prevention. Future research is challenged to develop algorithms capable of detecting cyberbullying from several multimedia sources.