Clustering of Student Expertise Fields Using the K-Means Algorithm (Case Study: STMIK Kaputama Binjai)
Bibliographic record
Abstract
Grouping students' fields of expertise in higher education is an important issue that can provide significant benefits for students and educational institutions. STMIK Kaputama is one of the universities that has students with various fields of expertise, but the absence of data that informs the field of expertise of students is very unfortunate. Research data was obtained through questionnaires distributed to students, which included information about study programs, Grade Point Average (GPA), and areas of expertise. Clustering analysis was conducted using Matlab software to validate and implement the clustering results. The results show that the K-Means algorithm is effective in grouping students into clusters that have similar characteristics. The first cluster consists of students with expertise in programming and database, the second cluster focuses on students with networking expertise, and the third cluster includes students with various combinations of expertise.This study also found a tendency that students with certain expertise have a higher GPA than students with other expertise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".