Peningkatan Kompetensi SMK dengan Keterampilan Computer Aided Design
Bibliographic record
Abstract
Human capitals in the computer field are required in the Industrial Era 4.0. Vocational schools (SMK) are aimed to produce graduates who are ready-to-use to work in industries. However, SMK graduates are one of the major contributors to high unemployment in Indonesia. Teachers with skills in the field of computer technology are scarce. In addition, the facilities and infrastructure are limited in many SMK schools, especially in rural areas. This community service activity was carried out at SMK Muhammadiyah Tirtayasa. The school is located in the northern coastal rural area of Serang Regency. This community service activity provided assistance to Computer Aided Design (CAD) skills by targeting teachers and students. CAD skills can provide skills for vocational graduates who will have careers in industry. The result showed an increase in the skills of participants in the field of 2D/3D CAD drawing. The activity was very fruitful and can be further followed up by both expanding the partner schools or integrating school curriculum which adopts CAD skills with the aim of developing further the CAD knowledge.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".