APLIKASI PERAMALAN JUMLAH KELAHIRAN DENGAN METODE JARINGAN SYARAF TIRUAN
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 forecast is a statistic analysis to predict what it will happen in the future using the data and information from the past. This research aimed to apply Artificial Neural Network method for estimate the f ertility rate in Surabaya. The study was descriptive which using secondary data providing from Dinas Kesehatan Kota Surabaya. The study used time series data by recapitulation of fertility rate monthly from 2012-2016. The data analysis used R Program. The result showed the best estimator model for Artificial Neural Network method was 1-3-1 architecture with preprocessing normalized. RMS value of Artificial Neural Network method was 338.1551. The conclusion of this research was the Artificial Neural Network method for estimate the f ertility rate in Surabaya could be used for planning birth control program especially Badan Kependudukan dan Keluarga Berencana Nasional.
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 | 0.007 | 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 | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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