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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Machine Learning and Intelligent Systems
Youssef Zaz
Abdelmalek Essaâdi University
Computer Science & IT

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Retrieval-augmented generation for Arabic legal information: the family code case study Jamal Hrimech; Mohammed Mghari; Youssef Zaz
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27400

Abstract

This document describes the implementation and evaluation of a retrieval-augmented generation (RAG) system to improve access to and understanding of Moroccan law, particularly the family code in Arabic. The research addresses the drawbacks of the widely used linguistic model applied to complex legal terminology in Arabic and aims to help citizens access crucial legal data. We built a new custom dataset with 2.5 k question-answer pairs while preprocessing and using the BGE-m3 embedding model in this experiment. Performance metrics, such as mean reciprocal rank (MRR), Recall@k, and F1-score, indicate that the RAG approach is effective compared to the use of standalone large language models (LLMs). Moreover, an evaluation on metrics such as the blue score, fidelity, response relevance, and contextual relevance indicated that the matching of meanings and context were well captured, which signifies a very good semantic understanding. The research highlights the need for language-specific model specialization in Arabic and presents its main challenges, such as dialectal variations and appropriate evaluation measures. The results indicate that well-developed RAG systems offer a promising approach to improving access to legal information in Arabic-speaking practice communities and to guiding future research and development in this field.
Optimizing Random Forest using Genetic Algorithm for static Android malware detection Youssef Zaz; Muhammad Taufiq; Barroon Isma'eel Ahmad
Indonesian Journal of Machine Learning and Intelligent Systems Vol. 1 No. 1 (2026)
Publisher : Indonesian Artificial Neural Network Society

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Abstract

The massive and increasingly complex growth of Android malware has led to a heightened threat to mobile device security. One proven effective approach is the use of machine learning, particularly the Random Forest algorithm. However, many previous studies have not fully utilized the potential of this algorithm, as they rely on default configurations without tuning. This study implements the Genetic Algorithm (GA) method to perform hyperparameter tuning on Random Forest, using the Drebin-215 dataset, which consists of 15,036 APKs and 215 static features. The study compares three configurations: Default and GA Tuning. Evaluation is conducted using 5-fold cross-validation and performance metrics including accuracy, precision, recall, and F1-score. The results show that the GA-Tuned model delivers the best performance, achieving an accuracy of 98.97%. The study also tests the model on 55 real APKs to evaluate its predictive capability in real-world cases.
Co-Authors Barroon Isma'eel Ahmad Jamal Hrimech Mohammed Mghari Muhammad Taufiq