Development of a prototype system integration model for RFID technology with the internet of things and its implementation to improve precast concrete material management in Indonesia
Bibliographic record
Abstract
The adoption of technology in Indonesian construction is still developing at a slower pace than in other sectors, particularly in materials management. Among the automation technologies that can be developed for precast material management, is integrated RFID technology with the Internet of Things (IoT). This study therefore aims to analyze the correlation between factors influencing the implementation of integrated RFID technology with IoT for precast material management in Indonesia and to examine the development model of the integrated RFID technology system with IoT. The research methods include a questionnaire survey with PLS-SEM and the development of technology systems validated by experts. The results demonstrate that factors, such as resource availability, implementation cost, stakeholder involvement, implementation risk, and project conditions have a significant direct and indirect impact on the implementation of integrated RFID technology with IoT for precast material management. Among these factors, the implementation of risk factors has the most significant influence. Furthermore, the development of the integrated RFID technology system with IoT has proven to be beneficial, especially as an automation technology for data recording and visibility in precast material management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".