Penerapan Algoritma Winnowing Untuk Mendeteksi Kemiripan Pada Karya Tulis Mahasiswa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Keberadaan Teknologi Informasi (TI) yang sedemikian berkembang, menyebabkan kegiatan plagiarism semakin berkembang pula. Hal ini cukup banyak dilakukan di kalangan akademisi di perguruan tinggi, yang dituntut untuk memenuhi kegiatan tri dharma khususnya kegiatan penelitian. Cikal bakal akademisi adalah mahasiswa-mahasiswa yang juga dituntut untuk melakukan penelitian juga. Dalam penelitian ini, dilakukan penerapan algoritma winnowing untuk mendeteksi kemiripan dokumen karya tulis mahasiswa Beberapa skenario pengujian dilakukan dengan mengubah nilai parameter gram dan window untuk mendapatkan nilai optimal. Nilai optimal ini akan diterapkan pada aplikasi yang akan dibangun.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 it