Design of Door Security System using Rfid Based on IoT at Stmik Kaputama
Bibliographic record
Abstract
The development of information technology has driven innovation in security systems, one of which is the development of door security systems based on the Internet of Things (IoT) and Radio Frequency Identification (RFID). This research aims to design and implement a Smart Door Lock system that allows real-time and remote access control through the integration of IoT and RFID technology. The system is designed using the ESP32 microcontroller connected to the MFRC522 RFID module, a relay for controlling the door lock, and indicators in the form of a buzzer and LED. Access data is stored locally and can be sent to a Telegram application via the Telegram Bot API to provide notifications of door activity. This research uses a Research and Development (R&D) method to produce a reliable, user-friendly, and efficient security system prototype. The test results show that the system successfully restricts access only to users who have registered RFID cards, and is able to automatically send access notifications to Telegram. Thus, this system can enhance security and convenience in door access management.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".