TEXT CLASSIFICATION OF BULLYING REPORTS USING NLP AND RANDOM FOREST. - Aldo, Dasril; Paramadini, Adanti Wido; Fathoni, M. Yoka (Jurnal Teknik Informatika (Jutif), 2025)
Bullying is a great concern that needs to be dealt with as early as possible, be it in the form of physical, verbal, social or cyber bullying. Using NLP algorithms, this paper intends to classify bullying report using Natural Language Processing in conjunction with Bag of Words. The study employs quantitative methodology. A total of 4671 reports of bullying are in essence categorized into physical, verbal, social, cyber and non-cyber bullying. We split the dataset into 80% training set (3737 reports) and 20% testing set (934 reports). The above model has achieved an accuracy of 94,76%, with good values of recall, precision and F1-score: 94,64%, 95,02% and 94,97% respectively. The dataset is then analyzed using Random Forest algorithm and Report of the Bullying Survey The model is to be effective in automatic Detection of Textual Bullying Reports Automated. While there has been no such effort in our institutions so far, automatic reporting of bullying will prove to be effective. This is because the system will allow a school or institution to have a precise constant monitoring of bullying reports. It will also allow an instantaneous action to be taken to protect the victim without letting the situation escalate.