Abstract— In the current digital era, information availability
is abundant, and news serves as a primary source of up-to-date
and reliable information for the public. However, with the
increasing volume of information, a robust evaluation method is
necessary to ensure accurate and dependable news labeling. This
research employs a machine learning approach, utilizing three
common classification algorithms: Naive Bayes, SVM, and
Random Forest, to evaluate news labels based on their titles. The
dataset utilized in this study is obtained from Jakarta AI Research
and consists of 10,000 samples covering various news topics.
Evaluation is conducted using accuracy, precision, recall, and F1-
Score metrics to gain a comprehensive understanding of the
classification algorithm's performance. The results of this
research demonstrate that the SVM algorithm exhibits the best
performance, achieving an accuracy rate of 92.92%. Random
Forest follows with an accuracy rate of 91.21%, and Naive Bayes
with an accuracy rate of 89.61%. These findings provide deep
insights into the effectiveness of the machine learning approach in
evaluating news labels based on their titles. Furthermore, the
study highlights the importance of considering other evaluation
metrics such as precision, recall, and F1-Score to obtain a more
holistic understanding of the algorithm's performance. Further
research is encouraged to involve additional classification
algorithms and more diverse and extensive datasets to enhance the
comprehension of news label evaluation comprehensively. Such
endeavors can significantly contribute to the development of
automated systems for classifying news with higher accuracy and
reliability in the future