PEMODELAN PENGISIAN PULSA LISTRIK PRABAYAR BERBASIS SHORT MESSAGE SERVICE (SMS)
Bibliographic record
Abstract
Smart Electricity or probaya electricity is a government program. Electricity is the main need of the community, without electricity, the economy is totally stuck, because many large factories and industries use electricity and depend on electricity. Prepaid kWh meter is one of the innovations that has been carried out by PLN in order to facilitate service to the community. Where the customer must pay in advance for the electrical energy that will be used, so that the use of electrical energy can be controlled by the customer according to their needs and abilities. People buy credit / electricity tokens then input the token code into Prepaid Meters (MPB), MPB automatically reads the serial token number and displays the number of kWh according to the amount purchased. The problem that often arises is the impractical process of inputting the token serial number into the MPB. This study made a SMS-based prepaid electricity charging model. The design of this MPB has three general parts, namely Modems that will message the homeowners, volt meter and ampere meter sensors to detect electrical power, and the brain, which is the microcontroller part of ATMega8535. This microcontroller will control all the running of the system contained in this MPB system. That is controlling the input system in the form of sensors, controlling the modem as a message reminder, controlling the input of electric current.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.066 | 0.030 |
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".