KLASIFIKASI TOKSISITAS KOMENTAR DENGAN ALGORITMA NAIVE BAYES DAN DECISION TREE
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Abstract
This study aims to develop a toxicity comment classification model using Naive Bayes and Decision Tree algorithms, specifically in the context of the online environment. The dataset consists of online comments, involving preprocessing steps such as text cleaning, normalization, and feature extraction using methods like TF-IDF. The Naive Bayes and Decision Tree classification models are trained on this dataset, and their performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Additionally, a comparative analysis between Naive Bayes and Decision Tree is conducted, focusing on the online context. This analysis aims to provide insights into their effectiveness in identifying toxicity in online comments. The findings of this study serve as a foundation for developing content moderation solutions that can adapt to the dynamic nature of human interactions in the online world. The results of this research have significant implications for building more efficient and effective content moderation systems in the online environment. By concentrating on the online context, the study makes a valuable contribution to understanding the performance of classification algorithms in addressing toxicity in online interactions. Consequently, the study's findings can help enhance user safety and comfort in the online environment through the development of more sophisticated content moderation solutions.
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