Penerapan Machine Learning Pada Sistem Informasi Klasifikasi Informasi Penggalian Potensi Pajak - Sukmana, Esha Indra; Nilawati, Lala (STMIK Banjarbaru, 2024)
The Directorate General of Taxes (DGT) faces challenges in managing and utilizing external data on the internet, such as news from online news portals to explore the potential of Taxpayers' taxes. This study produces an information system that automatically classifies news titles, based on their relevance to tax potential using machine learning algorithms. The algorithm chosen for this study is the CRISP-DM technique, which includes understanding business processes, collecting and exploring news data, text processing, and developing classification models using the BERT and distilBERT models. The results of the model evaluation matrix test show that the distilBERT model obtained an accuracy of 0.8763, precision 0.8776, Recall 0.8763, and F1-Score 0.8768. While the results for the confusion matrix obtained the highest accuracy, recall, precision, and F1-Score values with a value of 0.78. It is concluded that this model is the best, with balanced performance and higher metrics especially for the