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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 251 Documents
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A Systematic Analysis of the Impact of Non-Academic Factors on Student Academic Performance Prediction Using Data Mining Gabriella Caroline Prihayu Ningsih; Febri Liantoni; Yudianto Sujana
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3085

Abstract

This study investigates the prediction of students' academic performance using machine learning models through the analysis of 27 research articles. The primary objective is to identify a minimal set of essential features that significantly influence academic outcomes, aiming to optimize model performance and reduce data complexity. A Systematic Literature Review (SLR) was conducted following the PRISMA framework, focusing on key features such as midterm grades, faculty, department, demographic data, and, in some cases, behavioral attributes. The findings reveal that machine learning algorithms like Random Forest (RF) and Artificial Neural Network (ANN) consistently achieve high accuracy, surpassing 85% across various datasets, demonstrating their effectiveness in predicting academic performance. Feature selection methods, particularly filter-based techniques, were observed to significantly enhance the accuracy and efficiency of these models. Integrating diverse data, including dynamic learning behaviors, socio-economic factors, and campus attributes, is shown to further improve classification performance. Despite these advancements, challenges remain, particularly regarding the generalizability of machine learning models. Imbalanced datasets and limited dataset diversity often lead to reduced reliability when models are applied in broader contexts. Addressing these issues requires the development of more robust preprocessing techniques and advanced algorithms. The study also emphasizes the potential of deep learning models to further enhance predictive accuracy, as these approaches are capable of extracting more complex patterns from diverse datasets. Future research should prioritize expanding the scope of datasets to include a wider range of student populations and educational environments. These findings carry significant practical implications for educational institutions, enabling them to implement data-driven strategies for early intervention and personalized support. By identifying at-risk students and understanding factors influencing academic success, institutions can foster better educational outcomes and promote equitable learning opportunities.

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