JARINGAN SYARAF TIRUAN MEMPREDIKSI LAJU PERTUMBUHAN PENDUDUK KOTA BINJAI METODE BACKPROPAGATION
Bibliographic record
Abstract
Pertumbuhan penduduk yang sangat pesat sehingga mempengaruhi perekonomian dan tingkat pengangguran disuatu daerah yang memiliki tingkat pertumbuhan penduduk tersebut. Sehingga dibutuhkanlah suatu sistem yang dapat memprediksi jumlah pertumbuhan penduduk yang bertujuan untuk mengetahui berapa jumlah penduduk kota setiap tahunnya dengan mengunakan jaringan syaraf tiruan dengan metode Backpropagation. Data jumlah penduduk yang digunakan yaitu data tahun 2009-2018 yang berupa data setiap tahunnya. Dengan maksimum epoch antara 0-10000, learning rate 0.2 dan target error mulai dari 0.01. sampai dengan 56519.4 untuk mendapatkan hasil yang konvergen. Hasil prediksi laju pertumbuhan penduduk setelah melakukan proses pelatihan dan pengujian maka hasil prediksi laju pertumbuhan penduduk mengalami penurunan dengan rata-rata hasil prediksi 56516.9637 untuk mendapatkan hasil yang konvergen.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".