Prediction of Student Study Periods with Bidirectional GRU for On-Time Graduation Forecasting

Satriawan Rasyid Purnama; Henri Tantyoko; Etna Vianita

doi:10.14710/jmasif.16.2.73275

DOI: https://doi.org/10.14710/jmasif.16.2.73275

Prediction of Student Study Periods with Bidirectional GRU for On-Time Graduation Forecasting

Satriawan Rasyid Purnama

, Henri Tantyoko

, Etna Vianita

Department of Informatics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

Received: 15 May 2025; Revised: 26 Jun 2025; Accepted: 8 Jul 2025; Published: 10 Jul 2025.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

BibTex Citation Data :

@article{JMASIF73275,
    author = {Satriawan Purnama and Henri Tantyoko and Etna Vianita},
    title = {Prediction of Student Study Periods with Bidirectional GRU for On-Time Graduation Forecasting},
    journal = {Jurnal Masyarakat Informatika},
  volume = {16},
    number = {2},
    year = {2025},
    keywords = {Forcasting, Bidirectional Gated Recurrent Unit, Mean Absolute Percentage Error, Education},
    abstract = {  Delays in student graduation remain a persistent challenge in higher education, with approximately 28% of students requiring more than four years to complete their studies, exceeding the standard duration. This study addresses the issue by proposing a predictive model to estimate students’ graduation year using a Bidirectional Gated Recurrent Unit (BiGRU) neural network. The model is trained on a combination of academic and financial indicators, including Grade Point (GP) scores from the first to the fifth semester, cumulative Grade Point Average (GPA), and the single tuition fee tier (UKT). The integration of these features allows the model to learn temporal patterns in students’ academic progression and financial capacity. Empirical analysis reveals that students in the UKT 8 group consistently demonstrate superior academic performance, as evidenced by their higher average GPA across semesters, compared to students in lower UKT groups. The BiGRU model achieves a Mean Absolute Percentage Error (MAPE) of 9.5%, indicating high predictive accuracy. These findings highlight the potential of deep learning models, particularly BiGRU, in forecasting academic outcomes. Furthermore, the insights generated from this model can serve as a valuable tool for universities in formulating targeted academic interventions and policies aimed at promoting timely graduation and reducing dropout rates.  },
   issn = {2777-0648},  doi = {10.14710/jmasif.16.2.73275},
    url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/73275}
}

Citation Format:

Abstract

Delays in student graduation remain a persistent challenge in higher education, with approximately 28% of students requiring more than four years to complete their studies, exceeding the standard duration. This study addresses the issue by proposing a predictive model to estimate students’ graduation year using a Bidirectional Gated Recurrent Unit (BiGRU) neural network. The model is trained on a combination of academic and financial indicators, including Grade Point (GP) scores from the first to the fifth semester, cumulative Grade Point Average (GPA), and the single tuition fee tier (UKT). The integration of these features allows the model to learn temporal patterns in students’ academic progression and financial capacity. Empirical analysis reveals that students in the UKT 8 group consistently demonstrate superior academic performance, as evidenced by their higher average GPA across semesters, compared to students in lower UKT groups. The BiGRU model achieves a Mean Absolute Percentage Error (MAPE) of 9.5%, indicating high predictive accuracy. These findings highlight the potential of deep learning models, particularly BiGRU, in forecasting academic outcomes. Furthermore, the insights generated from this model can serve as a valuable tool for universities in formulating targeted academic interventions and policies aimed at promoting timely graduation and reducing dropout rates.

Fulltext

Keywords: Forcasting, Bidirectional Gated Recurrent Unit, Mean Absolute Percentage Error, Education

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Section: Research Article

Language : EN

In Vol 16, No 2 (2025): In Press

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