Bibliographic record
Abstract
This study grouped the interest of SMP Negeri 5 Binjai City students in extracurricular activities using the clustering method based on 2018–2024 data with variables of activity type, activeness, and achievement. The goal is to identify patterns of interest and utilize them for program management and development. The results are expected to help schools develop effective coaching strategies according to the characteristics of students. Education shapes character and develops students' potential not only academically, but also through extracurriculars. At SMP Negeri 5 Binjai City, extracurricular participation is not evenly distributed, affecting the effectiveness of supervisors and the development of activities. This study uses the clustering method to group students based on interests, activeness, and achievements, thereby helping schools manage and develop extracurriculars more effectively. This research is carried out in a structured manner through several stages: identification of problems to determine the focus of the research, collection of supporting and main data, study of related theories, analysis of data according to variables, testing and implementation of results, and evaluation to conclude findings and provide suggestions. This framework ensures that research is directed to produce useful results. This study succeeded in grouping the extracurricular interests of SMP Negeri 5 Binjai City students using the k-means algorithm with variables of activity type, activeness, and achievement through three cluster scenarios. Three clusters are sufficient for general strategies, while four or five clusters provide more specific coaching details, helping schools organize student motivational strategies, facilities, mentors, and programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".