PENERAPAN METODE STORY TELLING UNTUK MENINGKATKAN HASIL BELAJAR BAHASA INDONESIA (STUDI SISWA KELAS V SD INPES 12/79 LEA KECAMATAN TELLU SIATTINGE KABUPATEN BONE)
Bibliographic record
Abstract
Penelitian ini adalah penelitian PTK yang bertujuan untuk mendesripsikan penerapan metode story telling untuk meningkatkan hasil belajar Bahasa Indonesia siswa kelas V di SD Inpres 12/79 Lea Kecamatan Tellu Siatingge Kabupaten Bone. Subjek penelitian ini adalah seluruh siswa kelas V yang berjumlah 14 siswa dan guru wali kelas V. Setting penelitian ini bertempat di SD Inpres 12/79 Lea, jalan poros Wajo-Bone kecamatan Tellu Siattinge kabupaten Bone. Teknik pengumpulan data menggunakan observasi dan tes. Teknik analisis data yaitu mereduksi data, mendeskripsikan data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa Pada siklus I sebanyak 64,28% atau 9 siswa memperoleh nilai rata-rata 70,71 berada pada kategori cukup (C) dan mengalami peningkatan pada siklus II sebanyak 87,57% atau 11 siswa dengan nilai rata-rata 77,50 berapa pada kategori baik (B). Kesimpulkan penelitian ini adalah metode Story Telling dapat meningkatkan hasil belajar Bahasa Indonesia siswa kelas V SDN 12/79 Lea Kecamatan Tellu Siatingge Kabupaten Bone
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".