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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Rohayanti Hassan
Universiti Teknologi Malaysia
Computer Science & IT Engineering

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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 Adil Abbas Majeed Anis Farihan Mat Raffei Chan Shau Wei Choon Sen Seah Esraa Jaffar Baker Farid Zamani Che Rose Farid Zamani Che Rose Farizuwana Akma Zulkifle Haza Nuzly Abdul Hamed Hishammuddin Asmuni Lim Pui Jie Mohammad Aljanabi Mohammad Nazir Ahmad Mohd Arfian Ismail Mohd Arfian Ismail Mohd Farhan Md Fudzee Mohd Saberi Mohamad Mohd Saberi Mohamad Nada Thanoon Ahmed Noor Hayder Abdul Ameer Noor Hidayah Binti Zakaria Noor Hidayah Zakaria Noor Syahirah Nordin Nur Aqilah Khadijah Rosili Nur Ariffin Mohd Zin Nur Syaza Izzati Mohd Rafei Rashidah Omar Razib M. Othman Rd. Rohmat Saedudin Rodziah Atan Rohaizan Ramlan Rosfuzah Roslan Saad Abbas Abed Shahliza Abd Halim Shahreen Kasim Shahreen Kasim Sundos Abdulameer Alazawi Sundos Abdulameer Alazawi Tole Sutikno Zalmiyah Zakaria