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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Peuradeun Arabiyatuna : Jurnal Bahasa Arab Al-Ma'rifah : Jurnal Budaya, Bahasa dan Sastra Arab AL-AF'IDAH: Jurnal Pendidikan Bahasa Arab dan Pengajarannya Al-Hijr: Journal of Adulearn World Journal International of Lingua and Technology Lughawiyah: Journal of Arabic Education and Linguistics
Ardinal, Eva
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Religion Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Machine learning techniques for analyzing student’s performance in Islamic Studies Ramadani, Laili; Ardinal, Eva; Kamal, Muhiddinur; Ritonga, Mahyudin; Julhadi, Julhadi; Kardi, Juliwis; Nuraiman, Nuraiman
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.8029

Abstract

Many learning institutions and organizations are currently faced with the acute burden of trying to forecast the academic performance of their students. This paper reflects the application of machine learning tools to discuss the potential and performance of students in Islamic Studies. The framework suggested in this paper, will start with the acquisition of the historical data of the students in the input dataset. First, the forward selection wrapper method is used to select the most meaningful features thus eliminating the redundant qualities in the set of student data. Three types of classifiers are then used to create a classification model based on fuzzy support vector machines (SVM), K-nearest neighbors (K-NN), and Naive Bayes. In such a methodological approach, academic performance is predicted and results measured according to certain criteria. According to results of the experiment, it is noted that feature selection-fuzzy support vector machine (FS-fuzzy SVM) has an excellent accuracy of 99.9% with a sensitivity of 98.50% and a specificity of 98.50% and it is therefore seen to be more effective in predicting the academic performance of students in Islamic Studies.
Co-Authors Ahmad Fikri Ahmad Zuhdi Alamin, Nurul Cale, Woolnough Dina Destari Hadi, Al Amin Irawan Irawan Julhadi, Julhadi Juliwis Kardi Lutfiah, Husnah Mahyudin Ritonga Marlion, Ferki Ahmad Mudinillah, Adam Muhiddinur Kamal Nuraiman, Nuraiman Nurdianto, Talqis Ramadani , Laili Rijal Firdaus Rina Setyaningsih, Rina Sarip, Mohamad Syatria Adymas Pranajaya syukrawati, syukrawati Tauhid, Tauhid Zulfida, Sri