Pelatihan Pembuatan Robot Line Follower untuk Meningkatkan Pengetahuan Robotika pada Siswa SMK Negeri I Kramatwatu
Bibliographic record
Abstract
Robotics is one of the right media to introduce the field of technology to students because it can help in the development of thinking, sharpening capabilities in thinking, and the ability to form concepts. The development of robot technology is currently experiencing a rapid increase so it is important for students to gain robotics knowledge to face challenges in the era of industrial revolution 4.0. But not all schools have the facilities and human resources for learning robotics. SMK Negeri I Kramatwatu in the Serang area are currently studying robot technology but only in theory, so the results are not effective. This Community Service activity is carried out as an effort to improve the knowledge and skills of students at SMK Negeri I Kramatwatu in the form of training in making Line Follower robots, a type of mobile robot whose mission is to detect and follow a guideline that has been created in the field of trajectories. Line Follower Robot was chosen as training material because this robot is one type of automatic robot that is not too complicated in its manufacture. In this training, the participants were divided into 4 groups, each guided by one mentor. The results of community service show students' enthusiasm and desire to obtain knowledge in making Line Follower robots. This can be seen when tested on robots that have been made by 4 groups of participants, namely from 4 groups of participants 3 groups have succeeded in making a Line Follower robot that can run automatically in following the trajectory
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".