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Record W4415360397 · doi:10.59934/jaiea.v5i1.1367

Design of Door Security System using Rfid Based on IoT at Stmik Kaputama

2025· article· W4415360397 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2025
Typearticle
Language
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBuzzerAccess controlInternet of ThingsRadio-frequency identificationRelayData accessSecurity systemIdentification (biology)Microcontroller

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.283
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it