PERANCANGAN DAN PEMBUATAN ROBOT TARI PENDET KRSI 2010 (HARDWARE PENGGERAK LEHER, MATA, DAN PINGGUL)
Bibliographic record
Abstract
Pendet robot is a human-shaped robot made for the final assignment. This robot is capable of doing the dance of Bali island that is home Pendet. Making Robots Pendet KRSI 2010 was conducted to where the right momentum for a national echo evoke the love and preservation of national culture. Robots designed to use DC motors as the driving, the minimum AT89S52 microcontroller as the control system, driver circuit and a position sensor (potentiometer). Programming language using the language C that serves as the driving robot program as a whole. Downloaded program on the minimum system that will read Microcontroller AT89S52 rotation angle changes potentiometer sensors on the neck, eyes and hips robot when the motor rotates to the left (CCW) and to the right (CW), then the sensor on the neck, eyes and hips, the robot will be information ADC value (poisi) on the potentiometer so that the microcontroller will command the motor driver to drive the DC motor in the neck, eyes and hips in accordance with the amount of value that you set up the ADC. Final project created to discuss matters pertaining to the hardware (electronic system) and the mechanics of the robot. KEYWORDS: Microcontroller AT89S52, DC Motors, Potentiometers, Driver, Language C, ADC, Electronics, Mechanics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".