Pengelompokan Data Siswa Berdasarkan Profil Pelajar Pancasila Menggunakan Metode Clustering (Studi Kasus SMK Putra Anda Binjai)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengelompokkan data siswa SMK Putra Anda Binjai berdasarkan enam dimensi profil pelajar Pancasila menggunakan metode Clustering K-Means. Pengelompokan data siswa dilakukan dengan menghitung jarak menggunakan Euclidean Distance dan melibatkan tiga iterasi dalam analisis dengan variabel yang digunakan yaitu jurusan, nilai mata pelajaran, dan profil pelajar Pancasila. Sistem ini diimplementasikan dengan menggunakan aplikasi pemrograman MATLAB 2014a. Dari hasil proses dengan mengimplementasikan metode Clustering dan algoritma K-Means yang telah dilakukan dengan menggunakan 3 cluster data didapatkan kelompok data siswa atau grup yang memiliki karakteristik yang mewakili pola-pola tertentu dalam data siswa, seperti pola nilai dan profil pelajar Pancasila. Hasilnya diharapkan dapat membantu dalam pemahaman lebih lanjut tentang karakteristik siswa dan memberikan dasar bagi pengambilan keputusan di bidang pendidikan. Sistem pengelompokan data siswa yang dihasilkan juga memiliki kemudahan dalam penggunaannya.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".