PREDIKSI PENDAPATAN ASLI DAERAH (PAD) KABUPATEN LANGKAT MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK
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.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".