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Iwan Iskandar
Jurusan Kesehatan Lingkungan, Poltekkes Kemenkes, Tanjungpinang, Kepulauan Riau,, 29124
Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences

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Journal : Science, Technology, and Communication Journal

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A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Delifah, Nur; Harahap, Nazruddin Safaat; Agustian, Surya; Irsyad, Muhammad; Iskandar, Iwan
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Co-Authors Afriyanti, Liza Agnesti, Syafira Agustian, Surya Agustina, Auliyah Ahmad, Rizmah Zakiah Nur Alvinur Hidayat, Alvinur Amelia, Felina Ananda, Nuari Ananda, Sherly Arasy, Abdurrahman Astuti, Indah Dwi Aulia Ramadhani Budianita , Elvia Cynthia , Eka Pandu Darmizal, Teddie Delifah, Nur Elin Haerani Elvia Budianita Fadhilah Syafria Fauziyyah, Laila Nurul Febi Yanto Fikri, Mhd Ikhsanul Fitri Insani Gusrianti, Elsa Gusti, Siska Kurnia Hamwar, Syahbudin Handayani, Lala Syafitri Haryadi - Herdiana, Dora Horiza, Hevi Idris, M. Fadhil Idris, Muhammad Fadhil Iis Afrianty Irsyad (Scopus ID: 57204261647), Muhammad Jasril Jasril Kholilah Samosir Lawami, Reza Haya Lestari Handayani MF, M.Yusuf Muhammad Affandes Muhammad Fikry Muhammad Irsyad Mukodri, Dewi Mey Lestanti Muliani, Sarifah Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Permata, Rizkiya Indah Purnama, Irgi Putri. P, Zurneli Kurnia Ragil S, Mahdhan Reski Mai Candra Ridho Hilmawan, Ridho Rinaldi Daswito Ristina Rosauli Harianja rizkaramdhaniartie, siti Sandi, Wahyu Ari Sofiyah, Wan Suwanto Sanjaya Syaputra, Alviandy Tri Prastio Nugroho Yuhesti, Mutia Yuli Novita Sari, Yuli Novita Yusra Yusra, Yusra Zulya Erda