Implementasi Pendidikan Karakter Berbasis Pesantren dalam Meningkatkan Kualitas Kepribadian Peserta Didik (Studi Kasus di MINU KH. Mukmin, Sidoarjo)
Bibliographic record
Abstract
This study aims to analyze the implementation of Islamic Boarding School-Based Character Education (PKBP) which focuses on improving the personality qualities of students at MINU KH. Mukmin Sidoarjo. The study method used is descriptive qualitative by collecting data through participatory observation, in-depth interviews, and document techniques. The collected data were then analyzed using the theory from Milles and Huberman, which includes descriptive narrative, data reduction, data presentation, and conclusion. Data validity was checked through credibility, transferability, dependability, and confirmability tests. The results showed that the implementation of PKBP at MINU KH. Mukmin Sidoarjo involves four aspects: teaching and learning activities, extracurricular activities, Islamic boarding school religious activities implemented in schools, and supporting activities. To improve the quality of personality, focus is given to inculcating the values of religious character, honesty, discipline, tolerance, independence, hard work, curiosity, national spirit or nationalism, communication, fondness of reading, environmental and social care, and responsibility. The PKBP strengthening program is carried out in three ways, namely extracurricular, co-curricular and extracurricular programs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".