RANCANGAN PENEMPATAN ACCESS POINT UNTUK MENDUKUNG LAYANAN E-LEARNING DI AREA KAMPUS TEKNIK ELEKTRO UNIVERSITAS UDAYANA
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.
Bibliographic record
Abstract
The development of information technology in the field of education has developed very rapidly, e-learning is one example of the development of information technology of education. To optimize the e-learning system in an area, adequate network infrastructure is needed. This research aims to improve the WLAN (Wireless Local Area Network) network infrastructure in the campus area of the Electrical Engineering Study Program, Faculty of Engineering, Udayana University, Bukit Jimbaran. This research was conducted in 3 stages, namely measuring the capacity and range of access points directly, calculating the access point signal level, and using simulations to calculate the number and range of access points using the Atoll Rf Planning software. Based on the results of measurements and calculations obtained, the results of the 3 stages of the research carried out found the farthest range of indoor and outdoor access points each 12 meters and 22.6 meters. The total number of access points needed for indoor and outdoor positions as a whole in accordance with the simulation results requires as many as 22 access points.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it