Pelaksanaan Program Kelas Maya Berbasis Learning Management System di SMA Negeri 18 Palembang
Bibliographic record
Abstract
Penelitian ini dilatarbelakangi oleh kebutuhan SMA Negeri 18 Palembang sebagai sekolah penggerak untuk mengoptimalkan transformasi digital melalui pelaksanaan program kelas maya berbasis Learning Management System (LMS). Program ini menjadi solusi strategis atas keterbatasan ruang belajar pasca lonjakan peserta didik pada PPDB 2023, sekaligus mendorong inovasi pembelajaran yang lebih fleksibel dan berkelanjutan. Penelitian ini bertujuan untuk mendeskripsikan secara komprehensif pelaksanaan program kelas maya, mengidentifikasi kendala yang muncul dalam implementasinya, serta menganalisis solusi yang diterapkan sekolah guna meningkatkan efektivitas pembelajaran digital. Penelitian menggunakan metode kualitatif dengan pendekatan studi kasus, melalui teknik pengumpulan data berupa observasi, wawancara mendalam, dan dokumentasi. Analisis data mengikuti model Miles dan Huberman yang meliputi reduksi, penyajian, dan verifikasi data. Hasil penelitian menunjukkan bahwa pelaksanaan program kelas maya telah berjalan optimal melalui pengelolaan lima aspek utama: manajemen pengguna, manajemen konten pembelajaran, komunikasi dan kolaborasi, penilaian, serta pelacakan dan pelaporan. Guru dan siswa mampu memanfaatkan fitur LMS secara aktif, didukung pelatihan teknis dan pedoman konten yang terstandar. Meskipun demikian, ditemukan kendala berupa keterbatasan SDM dalam penyusunan laporan berkala, kendala teknis jaringan, serta variasi kemampuan digital pengguna. Kendala tersebut berhasil diatasi melalui pelatihan berkelanjutan, peningkatan infrastruktur internet, serta pendampingan intensif bagi guru. Secara keseluruhan, implementasi program kelas maya berbasis LMS di SMA Negeri 18 Palembang terbukti efektif mendukung mutu pembelajaran digital melalui integrasi teknologi, manajemen sekolah yang adaptif, serta peningkatan kapasitas guru dalam pengelolaan pembelajaran daring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".