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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Nur Aqilah Khadijah Rosili
Universiti Teknologi Malaysia
Computer Science & IT Engineering

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A systematic literature review of machine learning methods in predicting court decisions Nur Aqilah Khadijah Rosili; Noor Hidayah Zakaria; Rohayanti Hassan; Shahreen Kasim; Farid Zamani Che Rose; Tole Sutikno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp1091-1102

Abstract

Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. The machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.
Predicting child alimony under Islamic shariah law using hybrid fuzzy inference system Nur Aqilah Khadijah Rosili; Rohayanti Hassan; Noor Hidayah Binti Zakaria; Farid Zamani Che Rose; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1367-1375

Abstract

This paper proposes a novel approach to predicting child alimony under Islamic Shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1 score metrics. The hybrid fuzzy system achieved promising results with 88% accuracy, 84% precision, 89% recall, and an 86% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decision-making tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic Shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.
Co-Authors Farid Zamani Che Rose Farid Zamani Che Rose Noor Hidayah Binti Zakaria Noor Hidayah Zakaria Rohayanti Hassan Shahreen Kasim Shahreen Kasim Tole Sutikno