Klasifikasi Jenis Kejahatan berdasarkan Teks Amar Putusan Pengadilan Hukum Pidana KUHP menggunakan IndoBERT - Perdana, Tirtanusa Kurnia Adhi; Soyusiawaty, Dewi (Universitas Hamzanwadi, 2025)
The increasing number of the court’s rulings each year presents a challenge for the judiciary. One strategic solution is the application of Artificial Intelligence (AI). Indonesian-based models such as IndoBERT is potential to ease workloads by automatically classifying legal cases. This study aims to explore the capability of IndoBERT to automatically classifying the verdict of section of Indonesian KUHP rulings to accelerate crime type identification. This is an experimental study using supervised text classification. The dataset consists of 12000 verdicts collected from the Indonesian Supreme Court website, classified using IndoBERT fine-tuned with various hyperparameter configuration. Our findings show that the model with a batch size of 8 and learning rate 5e-5 achieved accuracy of 92.59%, precison of 92.93%, recall of 92.59%, and F1-Score of 92.59% on unseen test data. The high accuracy is supported by the explicit mention of crime types within verdict texts. To date, no study has specifically utilized IndoBERT or other models for automatic classification of KUHP articles. This finding has the potential to be integrated into the Supreme Court’s Directory of Decision as a support tool for automatic classification and legal document archiving.