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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi El-QUDWAH Gamatika Elkawnie Indonesian Journal of Electrical Engineering and Computer Science Mathematics and Applications (MAp) Journal Jurnal Riset Mahasiswa Matematika Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika Journal of Research on Community Engagement
Mohammad Jamhuri
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Humanities Biochemistry, Genetics & Molecular Biology Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Deep Neural Network-Based Student Performance Prediction with Hessian-Free Optimization Irawan, Andy; Abidin, Zainal; Jamhuri, Mohammad
Jurnal Riset Mahasiswa Matematika Vol 5, No 4 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i4.37951

Abstract

Predicting student graduation predicates is important for academic monitoring and timely intervention in higher education. This study investigates graduation predicate prediction using deep neural networks under three feature-group settings: academic-only, non-academic-only, and combined academicnon-academic features. A multilayer perceptron with three hidden layers was trained using SGD with momentum, RMSProp, Adam, and a damped Hessian-free optimization procedure. Two tasks were considered: a four-class graduation predicate classification task and a binary risk-screening task in which Sufficient was treated as the positive risk class. The results show that the combined feature group achieved the best multiclass performance, with an accuracy of 0.8478 and a weighted F1-score of 0.8274. Hessian-free optimization consistently produced the best results across all feature-group scenarios, with the clearest gain appearing in the non-academic-only setting. In the additional risk-screening analysis, non-academic variables provided meaningful but limited predictive signal, and Major emerged as the strongest individual predictor. These findings show that combining academic and non-academic information improves graduation predicate prediction and that Hessian-free optimization is an effective training strategy for deep neural classification in educational data.
Co-Authors Andy Irawan Ari Kusumastuti Binti Tsamrotul Fitria, Binti Tsamrotul Dewi Firdaus Eva Yuliana, Rossima Evawati Alisah Fatma Mufidah, Fatma Imam Mukhlash Imam Mukhlash Imam Sujarwo Juhari Juhari, Juhari Mariatul Ulya, Diah Mohammad Isa Irawan Mohammad Nafie Jauhari Mohammad Syaiful Arif, Mohammad Syaiful Mondal, Kartick Chandra Muhammad Khudzaifah Muhammad Thahiruddin Ni Nyoman Tri Puspaningsih Nurul Anggraeni Hidayati Pranata, Farahnas Imaniyah Sari, Silvi Puspita Siti Amiroch, Siti Subiono - Syafi'ah, Nurus Ulya, Diah Mariatul Usman Pagalay Utomo, Tri Vivi Aida Fitria Yuliana, Rossima Eva Zainal Abidin