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All Journal Teknika ITEj (Information Technology Engineering Journals) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) International Journal of Engineering, Science and Information Technology Algoritme Jurnal Mahasiswa Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Malikussaleh Mengabdi INOVTEK Polbeng - Seri Informatika
Zahratul Fitri, Zahratul
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Journal : ITEj (Information Technology Engineering Journals)

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Development of an Expert System for Identifying Students' Learning Styles Using the Euclidean Probability Method Rahma, Putri; Fitri, Zahratul; Fuadi, Wahyu
ITEJ (Information Technology Engineering Journals) Vol 10 No 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.214

Abstract

Learning styles play an important role in determining the most effective teaching strategies by aligning instructional methods with students’ individual preferences in receiving, processing, and understanding information. However, classroom teaching is often applied uniformly, disregarding the differences in learning styles among students. This can hinder the effectiveness of the learning process. This research aims to develop a web-based expert system using the Euclidean Probability method to identify the dominant learning styles of students at SMK Negeri 3 Lhokseumawe. The system processes input data representing student characteristics and calculates the proximity to each learning style category using the Euclidean distance formula. A total of 110 student data entries were analyzed, revealing that 32 students (29.09%) had a Visual learning style, 26 students (23.64%) were Auditory, 16 students (14.55%) were Read/Write, and 36 students (32.73%) were Kinesthetic learners. The results showed that the Kinesthetic learning style was the most dominant among students. Therefore, this expert system can efficiently assist in determining students' learning styles, allowing for quick and accurate identification of their learning preferences. This supports the development of more personalized and adaptive learning strategies, which are expected to enhance student engagement and learning outcomes.
Co-Authors - Fakhrurrazi Ade Irfan Aidilof, Hafizh Al Kautsar Akbar, Muhammad Zulfat Ananda Faridhatul Ulva Andik Bintoro Angga Pratama ANNISA KARIMA Asrianda Asrianda Bakhtiar Bakhtiar Dahlan Abdullah Delianda, Anggun Dita Amelia, Dita Emi Maulani Eva Darnila Fadliani, Fadliani Fuadi, Wahyu Hisyam, Mochammad Irwansyah, Defi Kausar, M Reza Khairul Amna Leni Maulida Liza Afra Merdu, Bunga Mawarni MUHAMMAD TEGUH, MUHAMMAD Muhibuddin Muhibuddin, Muhibuddin Mutammimul Ula Nisa, Miftahul Nunsina, Nunsina Nurdin Nurdin Nurdin Nurdin Nuriana, Nuriana Putri, Rizki Amelia Rahma, Putri Razi, Ar Rizal S.Si., M.IT, Rizal Safriana Safriana Safwandi Safwandi, Safwandi Samsuar, Samsuar Saptari, Mochamad Ari Siregar, Siti Chairani Ummi Kalsum Zainal Abidin Zul Akli