PENERAPAN INTERNET OF THINGS PADA SISTEM KONTROL PENERANGAN RUMAH BERBASIS WEB APPLICATION MENGGUNAKAN METODE LOGIKA FUZZY
Bibliographic record
Abstract
The need for electrical energy in everyday life is so important, improper and uncontrolled use can result in waste. Efforts to create energy-efficient homes need to start from simple things such as the use of lights, often forgetting to turn off the light switch when going out of the house or problems, especially in setting the brightness of the house lights, fuzzy logic provides an alternative solution. Fuzzy logic is a smart method that is appropriate for mapping a parameter input space into an output space for the brightness adjustment action of a lamp to light dimly, medium and bright. Efforts that can be made to deal with the problems above certainly require an intelligent control system with smart methods such as fuzzy logic and can be controlled remotely in order to overcome the existing problems. One concept that is popular right now is the Internet of Things (IoT), the development of IoT is a concept that aims to expand the benefits of internet connectivity that is connected continuously. IoT has capabilities such as data sharing, remote control, and so on. In building a control system by applying the IoT concept, a platform that supports that concept is also needed, NodeMCU a microcontroller which is equipped with a wifi module for microcontroller communication to the internet, so that the microcontroller can be used to apply the IoT concept in controlling home lights remotely, such as turning on the remote and turn off and adjust the brightness of the home lights with the fuzzy logic method intelligence.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".