Classification of Industrial Relations Dispute Court Verdict Document with XGBoost and Bidirectional LSTM

Abstract

Industrial relations disputes (Perselisihan Hubungan Industrial (PHI)) are essential to examine because these disputes represent unbalanced bargaining positions between workers and corporations. On the other hand, there are many PHI documents, so they need to be classified and distinguished from other types of other decisions for other types of civil cases. PHI decisions document can be accessed openly from a special directory of civil courts. This ruling has similarities with other decisions regarding consumer protection or bankruptcy. This study used 450 documents consisting of 255 PHI court decisions and 255 non-PHI court decisions. This study takes the case as a classified part. We use several feature extractions and three methods: Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Bidirectional Long Short-Term Memory (Bi-LSTM). For SVM and XGBoost classifier, we utilize Frequency-inverse document frequency (TF-IDF). Another classifier needs word embedding Glove Wikipedia Indonesian with a dimension size of 50. Various experiments conducted found that the best classification results used Bi-LSTM with Gloves. This classification has 100% accuracy without overfitting. We found the second result using XGBoost with parameters optimized using random search, while the lowest accuracy results were obtained using the SVM method. The accuracy of the classification results in this study can impact the availability and quality of open legal knowledge that can be utilized by society and for future research.