Penerapan Reward and Punishment melalui Tata Tertib Sistem Point dalam Meningkatkan Kualitas Pendidikan Karakter
Bibliographic record
Abstract
Dalam UU Sisdiknas No. 20 tahun 2003 pasal 1 ayat 1 bahwa Pendidikan adalah usaha sadar dan terencana untuk mewujudkan suasana belajar dan proses pembelajaran agar peserta didik secara aktif mengembangkan potensi dirinya untuk memiliki kekuatan spiritual keagamaan, pengendalian diri, kepribadian, kecerdasan, akhlak mulia, serta keterampilan yang diperlukan dirinya, masyarakat, bangsa dan Negara. Namun fakta di lapangan menunjukkan masih banyak permasalahan yang jauh dari harapan baik itu dari peserta didik Penyimpangan berbagai norma agama dan sosial kemasyarakatan dalam bentuk kurang hormat pada guru dan pegawai, kurang disiplin waktu, kurang mengindahkan peraturan, kurang memelihara keindahan dan kebersihan lingkungan, perkelahian antar pelajar, narkoba, berkeliaran di jalanan, di terminal bus, di stasiun, di mall dan tempat-tempat wisata saat jam pelajaran dan sebagainya hasil penelitian disimpulkan bahwa pemberian reward and punishment melalui tata tertib sistem point sangat efektif dalam meningkatkan kualitas karakter peserta didik dan direkomendasikan yakni 1. Pihak Madrasah, Khususnya kepala madrasah dan guru, hendaknya selalu berupaya mengevaluasi penerapan reward and punishment melaui tata tertib sistem point 2. Kementerian Agama hendaknya membuat kebijakan pada semua madrasah.3. Peserta didik penerapan reward akan memberikan semangat dan motivasi, punishment bagi peserta didik di jadikan sebagai filter atau kontrol tidak melakukan pelanggaran pada tata terib madrasah sehingga menjadi peserta didik memiliki kepribadian yang baik.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.007 |
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".