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All Journal Khazanah: Journal of Islamic Education and Science
Moch Nurcholis Majid
Universitas Uluwiyah Mojokerto
Education Other

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Application of Machine Learning Algorithms Based on Islamic Moral Values for Mitigating Negative Content on Digital Media Moch Nurcholis Majid; Muhammad Suhaili
Khazanah: Journal of Islamic Education and Science Vol. 1 No. 2 (2025): Khazanah: Journal of Islamic Education and Science
Publisher : Institut Bahri Asyiq Galis Bangkalan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61815/khazanah.v1i2.871

Abstract

Information bias in digital media has evolved into a systemic problem driven by algorithmic designs that prioritize user engagement optimization without adequate value orientation. This condition has contributed to polarization, amplification of sensational content, and unequal information representation. This study aimed to reconstruct data science algorithms based on Prophetic Ethics as a normative-operational framework to mitigate information bias in digital media. The research employed a descriptive qualitative approach with a systematic meta-analysis of scholarly publications from 2016 to 2025 retrieved from reputable academic databases. Data were analyzed using content analysis and thematic analysis to synthesize patterns of algorithmic bias and formulate a reconstruction model grounded in the principles of ṣidq (truthfulness), amānah (trustworthiness), tablīgh (transparency), and faṭānah (wisdom). The findings indicated that algorithmic bias occurred across all stages of the data processing pipeline and required structural reconstruction rather than partial technical adjustments. The proposed model integrated data validation, accountable governance, transparent modeling, and social welfare optimization within recommendation system design. This study contributed to the development of a value-oriented data science paradigm and expanded the discourse on algorithmic ethics in contemporary digital societies.
Co-Authors Muhammad Suhaili