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All Journal International Journal of Recent Technology and Applied Science (IJORTAS) Harmony Philosophy: International Journal of Islamic Religious Studies and Sharia
Eka Pandu Cynthia
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Religion Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology

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Evaluating the Role of Maqāṣid al-Sharīʿah in Formulating Ethical Frameworks for Artificial Intelligence in Islamic Jurisprudence Wiwik Hidayati; Eka Pandu Cynthia
Harmony Philosophy: International Journal of Islamic Religious Studies and Sharia Vol. 1 No. 2 (2024): May: Harmony Philosophy: International Journal of Islamic Religious Studies and
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/harmonyphilosophy.v1i2.323

Abstract

The rapid advancements in Artificial Intelligence (AI) have raised significant ethical concerns across various sectors, necessitating the need for robust ethical frameworks to guide their development and implementation. This study explores the intersection of AI ethics and Islamic law, focusing on how Maqāṣid al-Sharīʿah, the higher objectives of Islamic law, can be applied to AI governance. By examining key Islamic principles such as justice, transparency, privacy, and human dignity, the study investigates how these values can provide a moral compass for addressing AI-related ethical challenges, such as algorithmic bias, privacy violations, and the erosion of human autonomy. The Maqāṣid al-Sharīʿah framework offers a proactive and vision-oriented approach, prioritizing societal well-being while ensuring the alignment of AI technologies with Islamic moral standards. Unlike traditional Islamic legal responses, which are often reactive and case-specific, the Maqāṣid approach promotes the anticipatory evaluation of technologies, emphasizing the need for a balance between technological innovation and ethical responsibility. The paper also discusses potential solutions to bridge the gaps between global AI ethics frameworks and Islamic ethical standards, including interdisciplinary collaboration and the development of hybrid regulatory models. Additionally, it highlights the need for continuous updates to Islamic legal frameworks to address emerging technological issues, ensuring that AI systems are ethically sound, Shariah-compliant, and beneficial to society. This study aims to contribute to the growing discourse on the ethical implications of AI from an Islamic perspective, offering insights into how Islamic law can play a crucial role in shaping the future of AI governance.
Explaining Cholesterol-Related Coronary Artery Disease Risk Using Machine Learning and SHAP Eka Pandu Cynthia; Suzani Mohamad Samuri; Wang Shir Li; Alabbas Hussein Saeed; Inggih Permana; Febi Yanto
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 1: March 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0801.920

Abstract

Coronary Artery Disease (CAD) remains a leading cause of global mortality, with dyslipidemia recognized as a major modifiable risk factor. This study investigates the relationship between serum lipid parameters and CAD using the Z-Alizadeh Sani clinical dataset comprising 303 patients with 55 clinical, biochemical, and electrocardiographic attributes. Logistic Regression (LR) and Random Forest (RF) models were developed to predict CAD status, supported by a standardized preprocessing pipeline, multi-split train–test evaluation (70/30, 80/20, 90/10), and performance assessment using Accuracy, Precision, Recall, F1-Score, and AUC-ROC. SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability and quantify the contribution of lipid-related and clinical features to individual predictions. The RF model consistently outperformed LR across all split configurations, achieving a maximum AUC of 0.96, while LR attained an AUC of 0.90. SHAP analysis revealed that total cholesterol (CHOL) and low-density lipoprotein (LDL) were strong positive predictors of CAD, whereas high-density lipoprotein (HDL) exhibited a protective effect, in line with established cardiovascular pathophysiology. These findings demonstrate that integrating explainable machine learning with routine clinical lipid profiles can provide accurate and transparent decision support for early CAD risk stratification.
Co-Authors Alabbas Hussein Saeed Febi Yanto Inggih Permana Suzani Mohamad Samuri Wang Shir Li Wiwik Hidayati