JARINGAN SARAF TIRUAN UNTUK MEMPREDIKSI JUMLAH PASIEN RAWAT JALAN BAGI PENGGUNA NARKOBA MENGGUNAKAN METODE BACKPROPAGATION (STUDI KASUS : KANTOR BNN KOTA BINJAI)
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
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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