Heuristic-Based Backpropagation Algorithm Parameter Optimization to Improve Accuracy Study Program Prediction as an Effort to Achieve Quality Education
Bibliographic record
Abstract
Predicting students' study programs based on heterogeneous academic and personal attributes remains a complex challenge in educational data mining.Conventional neural network models that rely solely on academic parameters often suffer from misclassification and weak generalization.This study proposes a heuristic-based backpropagation optimization framework that combines a Genetic Algorithm (GA) with an adaptive Fusion- mechanism to enhance Multi-Layer Perceptron (MLP) performance.The GA adaptively tunes learning rate, momentum, batch size, and neuron configuration, while Fusion- balances the contributions of academic (grades in mathematics, English, and Indonesian) and non-academic features (interests, personality traits, and learning styles).Using a dataset of undergraduate students from Universitas Katolik Santo Thomas Medan (class of 2024), the proposed GA-Fusion- model was trained for 50 epochs under a stratified data-split setting.Experimental results reveal an accuracy improvement from 47.37% to 52.63%, corresponding to a 5.26% absolute and 11.1% relative gain.Although the improvement appears modest, it is educationally meaningful, as it reduces program misplacement errors by nearly 10%, which directly enhances academic guidance and admission decisions.The results indicate that heuristic-guided parameter optimization improves model stability, reduces overfitting risks, and provides a methodologically novel pathway toward developing adaptive, fair, and data-driven educational recommender systems that support the goal of quality higher education (SDG 4).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".