Configuration of the Values of Al-Qur’an, Pancasila, and Mathematics: Critical Study of Integration-Based Analysis in Indonesia

Evita Yuliatul Wahidah, Syaik Abdillah, Roni Sahroni, Sangkot Sirait, Erika Setyanti Kusumaputri

Abstract


This paper presents about the values of Al-Qur’an, Pancasila, and Mathematics in the discourse on the theory of interconnection integration undergoing a demystification process. The configuration of the values of Al-Qur’an, Pancasila, and Mathematics through Islamic studies is carried out with a theo-anthropocentric process on the integralist values of Al-Qur’an to Mathematics, giving birth to six models, namely Mathematics for Al-Qur’an, Mathematics from Al-Qur’an, Mathematics to explore Al-Qur’an, Mathematics to deliver Al-Qur’an, Mathematics to explain Al-Qur’an, and Mathematics with Al-Qur’an. In integration, there is an effort to restructure Qur’anic and Mathematical sciences, while in interconnection there are meetings in the format of complementation, comparison, confirmation, and the contribution of the verses of Al-Qur’an to the five precepts of Pancasila.

Keywords


Configuration; Value; Pancasila; Mathematic; Al-Qur’an

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DOI: http://dx.doi.org/10.21043/addin.v17i2.22487

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