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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informatika Global Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Darma Agung Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Brilliance: Research of Artificial Intelligence JAPI: Jurnal Akses Pengabdian Indonesia Knowbase : International Journal of Knowledge in Database Ranah Research : Journal of Multidisciplinary Research and Development International Journal of Multidisciplinary Approach Research and Science Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Khazanah Journal of Religion and Technology
Adi Purnama
Universitas Widyatama, Bandung, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Transportation Other

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Thematic Grouping of Quranic Verse Translations Based on Word2Vec and K-Means Clustering Al Husaeni, Ahmad Badru; Putra, Alif Firmansyah; Purnama, Adi; Lerian, Adly Juliarta; Fathurohman, Diman
Khazanah Journal of Religion and Technology Vol. 3 No. 2 (2025): December
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kjrt.v3i2.1748

Abstract

This study aims to group thematically translated texts of Indonesian Quranic verses using a Word2Vec-based machine learning approach and the KMeans Clustering algorithm. The process begins with text preprocessing, creating vector representations using Word2Vec, and then clustering using KMeans with quality evaluation using the Silhouette Score metric. The experimental results show that the model is able to form six main thematic clusters that semantically describe themes such as prayer and hope, moral evil, social law, the teachings of revelation, divinity, and the stories of figures and ethics. Two-dimensional visualization with PCA strengthens the interpretation of the formed clustering patterns. This study proves that the unsupervised learning approach can be relied upon to support the automation of digital thematic interpretation objectively and systematically. In addition, the results of this clustering have the potential to become the basis for the development of topic-based verse search systems, contextual Quranic learning applications, and technology-based exploration of Islamic studies. This study also supports the achievement of Sustainable Development Goals (SDGs) point 4 regarding increasing access to inclusive and quality education through information technology.
Co-Authors Adit Oktopryadin Al Husaeni, Ahmad Badru Alif Nuryana Atep Aulia Rahman Aulia, Rahma Bagus Alit Prasetyo Benny Yustim Calvin Matulessy Candimadam Esa Fauzi Fathurohman, Diman Herlistiono, Iwa Ovyawan Husni Robani Huynh, Phuoc-Hai Indriani Kautsar, Almila Aulia Kusramdani, Rizky Lerian, Adly Juliarta Luhur Bayuaji, Luhur Nuryana, Alif Pangestu, Rizky Bagus Putra, Alif Firmansyah Robani, Husni Sriyani Violina Suharno, Mochammad Syahrindra Akbar Suhendar, Sidik Tumaruk, Andry Septian Syahputra Viddi Mardiansyah Widyanto, Adi Yan Puspitarani Yenie Syukriyah