Implementation of Natural Language Processing in the Reporting and Handling System of Sexual Violence Cases on Campus
Abstract
Sexual violence in the campus environment is a serious problem that requires an effective reporting and handling system. This research aims to develop a Natural Language Processing (NLP)-based system that can improve the process of reporting and handling cases of sexual violence on campus. The methodology used includes the application of NLP techniques such as sentiment analysis and entity recognition to automate the identification and handling of reports. The Support Vector Machines (SVM) algorithm is used for the classification of text in this system. The data is collected from various sources, pre-processed, and used to train NLP models. The results of the study show that the system developed has an accuracy level of 91%, precision of 93%, and recall of 87%, which illustrates its effectiveness in collecting reports of sexual violence anonymously and accurately. Feedback from early adopters shows that the system improves the efficiency and accuracy of the reporting process. The conclusion of this study is that the implementation of NLP can significantly improve the reporting and handling system of sexual violence on campus. Further research is suggested to expand the scope of the system and improve its analysis capabilities.
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