PENGARUH MODEL CONCEPT SENTENCE DENGANBERBANTUAN MEDIAAUDIO VISUAL TERHADAPKETERAMPILAN MENULIS TEKS BERITA SISWAKELAS XI SMA RK DELI MURNI BANDAR BARU
Bibliographic record
Abstract
Penelitian ini bertujuan untuk (1) menganalisis keterampilan menulis teks berita siswa kelas XI SMA RK Deli Murni Bandar Baru tanpa menggunakan model concept sentence berbantuan media audio visual pada kelas kontrol, (2) menganalisis keterampilan menulis teks berita siswa kelas XI SMA RK Deli Murni Bandar Baru dengan menggunakan model concept sentence berbantuan media audio visual pada kelas ekperimen, dan (3) menganalisis pengaruh model concept sentence dengan berbantuan media audio visual terhadap keterampilan menulis teks berita siswa kelas XI SMA RK Deli Murni Bandar Baru. Metode yang digunakan dalam penelitian ini adalah metode eksperimen dengan desain two group posttest only control design. Populasi penelitian ini adalah seluruh siswa/i kelas XI SMA RK Deli Murni Bandar Baru dengan sampel sebanyak 32 pada masing-masing kelas, yaitu kelas XI-B dan XI-D. Instrumen penelitian ini adalah instrumen penilaian keterampilan menulis teks berita. Data penelitian di analisis dengan teknik uji statistik deskriptif, uji normalitas, uji homogenitas, dan uji hipotesis. Hasil penelitian ini menunjukkan bahwa model concept sentence dengan berbantuan media audio visual memiliki pengaruh yang positif terhadap keterampilan menulis teks berita siswa. Hal tersebut dibuktikan dari nilai rata-rata posttest siswa di kelas kontrol sebesar 67,80 mencapai kategori cukup, sedangkan di kelas eksperimen nilai rata-rata posttest siswa lebih besar, yaitu 82,70 mencapai kategori baik. Selain itu, uji hipotesis memperoleh hasil bahwa thitung ttabel, yaitu 6,31 1,69. Hasil tersebut membuktikan bahwa model concept sentence dengan berbantuan media audio visual berpengaruh terhadap keterampilan menulis teks berita siswa kelas XI SMA RK Deli Murni Bandar Baru
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".