Classification of Factors Causing the Decline in Student Learning Achievement in The Post-Pandemic Period Using the C4.5 Algorithm (Case Study: STMIK Kaputama)
Bibliographic record
Abstract
The COVID-19 pandemic that has hit Indonesia since 2020 has brought significant changes to various aspects of life, including the learning system in universities. Universities, which originally implemented face-to-face learning processes, were forced to adapt to online learning. However, this change causes various obstacles, especially for students who experience learning loss, namely a decrease in interest and motivation to learn which has an impact on academic achievement. This research aims to classify the factors that cause the decline in student learning achievement in the post-pandemic period at STMIK Kaputama using the C4.5 algorithm. Using data from STMIK Kaputama students as a sample, this research analyzes various factors such as access to technology, involvement in online learning, participation in face-to-face learning, social support, learning motivation, economic conditions, family, college, and academic stress levels. It is hoped that the results of this research will provide a deeper understanding of the dominant factors that cause a decline in learning achievement, as well as become a reference for educational institutions in developing strategies to overcome the negative impacts of online learning during the pandemic Keywords: C4.5 Algorithm, Decrease in Achievement, Data Classification
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".