Court Decision Prediction Model Using Natural Language Processing and Random Forest

Abstract

The increasing number of criminal cases in Indonesia, which reached 288,472 in 2023, or rose by 15% from the previous year, has created a substantial workload for judicial professionals. This situation highlights the urgent need for artificial intelligence–based decision support systems to accelerate and improve the quality of legal decision-making. This study proposes a court decision prediction approach using the Random Forest algorithm combined with Natural Language Processing (NLP) techniques. The dataset consists of 21,630 court decisions from the Supreme Court of Indonesia, originally in PDF format and converted into XML. The research procedure includes text preprocessing, feature construction using Word2Vec and Fast Text, and Random Forest classification. Unlike previous studies employing LSTM, BiLSTM, and CNN methods with accuracy ranging from 49.14% to 77.32%, the proposed approach delivers better performance. Experimental results show that the model achieves a prediction accuracy of up to 63%-81% for Penalty Categories classification and up to 65%-80% for long punishment regression. These findings demonstrate the significant potential of applying NLP and Random Forest to develop predictive systems in Indonesian legal document analysis.