PREDIKSI PENDAPATAN ASLI DAERAH (PAD) KABUPATEN LANGKAT MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK
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 District Government of Langkat in this case regulates and manages Regional Original Income (PAD), for example, taxes can be increased by intensification and extentification. Extensification is an expansion of the type of tax, but several studies show that controlling the potential by expanding the type of tax does not stimulate interest and even creates reluctance for investors to invest in the area. Intensification is an effort to increase tax collection. This effort requires the ability of the regions to be able to correctly identify local revenue and the ability to collect taxes based on benefits and principles of justice. By inputting training data and training targets, the artificial neural network to predict the number of Langkat Regency using the backpropagation method can predict the amount of PAD in Langkat Regency. The artificial neural network system can recognize training data and target data with an iteration of 488 target error of 0.5 and a leraning rate of 0.1, resulting in a prediction of the amount of PAD in 2020, which is Rp. 140,948,000,000.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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