RANCANGAN SISTEM NOTIFIKASI KEDATANGAN PEMBELI DENGAN SUARA MENGGUNAKAN ARDUINO
Bibliographic record
Abstract
A grocery store is a place to sell various household needs. Which provides necessities such as kitchen needs, bathing needs, school equipment, snacks, and others. A place that provides so many things is certainly not spared from theft by several members of the community. In previous research, is a system for home security. This research focuses on the installation of multisensors at each entrance in the house, which uses buzzer sensors and SMS as outputs. Therefore, to adjust the conditions at the grocery store. The components consist of Arduino UNO R3, ultrasonic sensor, DF Player, LCD, I2C, Speaker, and ESP32-CAM. This system is equipped with the feature of taking photos sent to telegram using the ESP32-CAM. The results of this study are the distance that the sensor can detect is quite far, but as a form of experiment, the author limits the distance to 12cm and will detect objects when they are at a distance of 5cm. The test proves that when the sensor detects an object with the object status coming, the speaker sounds which is connected to the DF Player by playing audio stored on the sdcard. The LCD will display the status of "there" when there is an object in the store. At that time the ESP32-CAM will take a picture and send it to a telegram. When the object passes through the object there is no response from any other component other than the LCD, which displays a “none” status which means that no object is in the store.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.026 |
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".