PENERAPAN METODE CLUSTERING UNTUK PENGELOMPOKAN DATA PESERTA DIDIK BARU (PPDB) DI SMP SWASTA GOTONG ROYONG KUALA
Bibliographic record
Abstract
Sekolah SMP Swasta Gotong Royong Kuala merupakan suatu yayasan atau lembaga pendidikan yang menerima siswa siswi baru setiap ajaran baru telah tiba. Siswa siswi yang mendaftar di sekolah SMP Swasta Gotong Royong Kuala bersumber dari lulusan sekolah dasar yang ada di sekitar Kecamatan Kuala hingga luar Kecamatan Kuala. Banyaknya jumlah pendaftar dari tahun ketahun, hal ini menyebabkan data atau berkas yang bertumpukan yang hanya disimpan pada lemari penyimpanan berkas yang ada disekolah. Berkas tersebut terkadang hanya dibuka ketika ingin mencari sebuah informasi saja. dengan kata lain berkas yang disimpan kurang memiliki manfaat yang lebih. Misalnya data tersebut dapat diolah dan dapat dijadikan sumber informasi yang baru dengan menggunakan teknik data mining. Data mining dapat membantu Sekolah dalam menggali pengetahuan baru dengan cara memproses data yang ada dengan metode clustering dan menggunakan algoritma K-Means.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".