JARINGAN SARAF TIRUAN UNTUK MEMPREDIKSI JUMLAH PASIEN RAWAT JALAN BAGI PENGGUNA NARKOBA MENGGUNAKAN METODE BACKPROPAGATION (STUDI KASUS : KANTOR BNN KOTA BINJAI)
Bibliographic record
Abstract
Badan Narkotika Nasional Kota Binjai memiliki tugas dan fungsi sebagai pencegah penyalahgunaan terhadap narkotika, pemberantasan peredaran gelap narkotika, dan rehabilitasi bagi para pecandu narkotika di Kota Binjai. Badan Narkotika Nasional juga bertugas menyusun dan melaksanakan kebijakan nasional mengenai pencegahan dan pemberantasan penyalahgunaan dan peredaran gelap psikotropika, prekursor dan bahan adiktif lainnya kecuali bahan adiktif untuk tembakau dan alkohol. Sehingga dibutuhkan suatu aplikasi yang dapat meramalakan jumlah kunjungan pasien rawat jalan. Berdasarkan proses analisa yang telah dilakukan bawah sistem jaringan saraf tiruan dengan menggunakan metode Backpropagation dapat diimplementasikan kedalam aplikasi jaringan saraf tiruan dan menghasilkan prediksi pasien rawat jalan pengguna narkoba dengan rata-rata pengguna inex sejumlah 93 pasien, pengguna ganja sejumlah 78 pasien dan shabu 92 pasien dengan hasil 0,302960 sama dengan 30.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".