Classification Of Students Based On Factors That Affect Student Learning Achievement Using The K-Means Clustering Algorithm (Case Study: STMIK Kaputama Binjai)
Bibliographic record
Abstract
In the world of education, students are the main object of every educational implementation that always prioritizes disciplines that are beneficial to the students themselves. However, in lecture activities there are students who are diligent in participating in lecture activities and there are also those who rarely participate in lecture activities, this can be caused by internal and external factors, so that there can be significant variations in student learning achievements, with some achieving high grades, while others face difficulties in achieving the same achievements. Based on the description of the problem, the researcher conducted a study that aimed to group students based on factors that affect student learning achievement using the k-means clustering algorithm. The results of the research conducted produced 3 clusters with cluster 1 there were 5 data, the group of students with a very satisfactory predicate GPA (3.50-4.00), supported by both internal and external factors (interval 3.1-4). Cluster 2 has 3 data, the group of students with a satisfactory predicate GPA (3.00-3.49), supported by both internal and external factors (interval 2.1-3), and cluster 3 has 5 data, the group of students with a satisfactory predicate GPA (3.00-3.49), supported by both internal and external factors (interval 3.1-4).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 0.001 |
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".