Genetic K-Means Algorithms, ASSU Analisis Peningkatan Kompetensi Mahasiswa Menggunakan Model Pembelajaran ASSURE berbasis Project-Based Learning
Bibliographic record
Abstract
Sistem pembelajaran yang telah diterapkan dan dikembangkan bertujuan untuk meningkatkan, menguasai, memahami, dan menerapkan materi belajar untuk kemudian dijadikan suatu kompetensi dasar. Penelitian ini menganalisa hasil peningkatan kompetensi mahasiswa dalam mata kuliah teknologi informasi di STIKes Wira Medika Bali pada jenjang S1 Keperawatan dengan menggunakan sistem pembelajaran ASSURE berbasis Project-Based Learning. Metode yang digunakan dalam pengelompokkan hasil peningkatan tersebut menggunakan Genetic K-Means Algorithms, Metode ini dipilih karena mempunyai kinerja lebih optimal dari K-Means sederhana. Algoritma ini yang menggunakan natural selections untuk opitimalisasi menentukan initial seeds. Penentuan jumlah cluster yang digunakan dalam penelitian ini sebanyak tiga cluster dengan kategori tinggi, sedang dan rendah. Hasil dari penelitian ini untuk kategori sedang meningkat dengan range 3,92% dan 14%, untuk kategori rendah meningkat 31,37% dan 74%, untuk kategori tinggi menurun 35,29% dan 60%.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".