IMPLEMENTASI PEMBELAJARAN DARING BAHASA INDONESIA DI SMP NEGERI 1 JEBUS BANGKA BARAT
Bibliographic record
Abstract
Tujuan dari penelitian ini, yaitu: (1) mendeskripsikan Rencana Pelaksanaan Pembelajaran (RPP) daring bahasa Indonesia, (2) mendeskripsikan sistem pelaksanaan pembelajaran daring bahasa Indonesia. Penelitian ini menggunakan metode deskriptif wawancara, observasi, angket, dan dokumentasi dengan pendekatan kualitatif. Teknik pengumpulan data menggunakan teknik simak dan teknik catat. Sasaran penelitian ini adalah guru bahasa Indonesia dan peserta didik kelas VII, VIII, dan IX. Data hasil yang didapatkan berupa hasil observasi, wawancara, angket peserta didik, dan dokumentasi. Hasil penelitian menunjukkan bahwa (1) rencana pelaksanaan pembelajaran daring bahasa Indonesia disesuaikan RPP khusus pembelajaran daring Kurikulum 2013 yang telah disediakan oleh Kemendikbud sesuai dengan Surat Edaran Nomor 14 Tahun 2019 mengenai “Penyederhanaan Rencana Pelaksanaan Pembelajaran (RPP)”, (2) sistem pelaksanaan pembelajaran daring menggunakan model pembelajaran daring interaktif berbasis aplikasi Zoom Meeting, Google Meeting, Google Classroom, WhatsApp, dan sebagainya. Guru juga menerapkan model pembelajaran daring sinkronus agar dapat mewujudkan interaksi yang baik dengan peserta didik. Guru juga menggunakan strategi pembelajaran daring ekspositori dan inkuiri dengan diterapkan model discovery learning dan model problem based learning karena menyesuaikan kendala yang dialami oleh peserta didik.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".