ANALISIS PELAKSANAAN PRAKTIKUM MENGGUNAKAN KIT IPA FISIKA DI SMP SE-KECAMATAN SOJOL KABUPATEN DONGGALA
Bibliographic record
Abstract
Telah dilakukan analisis terhadap 10 guru yang mengajar mata pelajaran IPA fisika kelas VII, kelas VIII dan IX di 10 SMP Negeri di se-kecamatn Sojol. Responden tersebut dipilih dari sekolah yang memiliki laboratorium dan tidak memiliki laboraturium IPA. Penelitian ini bertujuan untuk mengetahui pelaksanaan praktikum menggunakan KIT IPA fisika di Sekolah Menengah Pertama se-Kecamatan Sojol. Data penelitian ini dikumpulkan menggunakan kuisioner dan dianalisis secara deskriptif. Hasil penelitian ini menunjukkan bahwa persentasi pelaksanaan praktikum menggunakan KIT IPA Fisika sangat kurang. Berbagai hal yang menyebabkan rendahnya persentasi pelaksanaan praktikum ini yaitu :1) intensitas guru dalam mengikuti pelatihan laboratorium masih kurang, 2) ketersediaan alat dan bahan praktikum masih kurang, 3) materi pelajaran IPA cukup padat sehingga guru lebih memilih metode ceramah, 4) tujuan pembelajaran sulit dicapai melalui praktikum 5) dibutuhkan waktu khusus untuk persiapan sebelum praktikum dilaksanakan, 6) waktu pelaksanaan praktikum dalam jam tatap muka selalu tidak mencukupi,7) pemahaman guru terhadap konsep serta penggunaan alat-alat praktikum masih rendah, 8) guru sulit merancang LKS sendiri, 10) tidak adanya laboran dan laboratorium yang dapat membantu pelaksanaan praktikum IPA fisika. Kata Kunci: Praktikum Fisika, Sekolah Menengah Pertama (SMP) se-Kecamatan Sojol dan KIT IPA Fisika
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".