PENERAPAN JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI PENJUALAN MOBIL DENGAN MENGGUNAKAN METODE BACKPROPAGATION (Studi Kasus : Toyota Auto 2000 Medan)
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
Penjualan kendaraan merek Toyota ditangani oleh Divisi Kendaraan yang berkedudukan di kantor pusat Jakarta dan untuk seluruh cabang ditangani oleh Departemen Penjualan. Data produksi yang digunakan adalah data tahun 2016, 2017 dan 2018 berupa data bulanan. Dengan epoch maksimum antara 0-10000, learning rate 0,1 dan target error 0,01-0,5 untuk mendapatkan hasil yang konvergen. Data penjualan mobil dapat dikenali oleh sistem jaringan syaraf tiruan dengan metode backpropagation, hasil pengujian mengalami kenaikan dan penurunan. Prediksi penjualan New Agya meningkat rata-rata 5.99/bulan, Calya meningkat rata-rata 5.99/bulan, All New Rush meningkat rata-rata 12.06/bulan, New Avanza meningkat rata-rata 7.72/bulan, New Vios menurun rata-rata 0.33/bulan, New Corolla meningkat rata-rata 0.13/bulan, New Camry menurun rata-rata 0.48/bulan, Etios menurun rata-rata 0.60/bulan, Yaris menurun rata-rata 0.57/bulan, New Yaris menurun rata-rata 3.38/bulan, Rush menurun rata-rata 3.12/bulan, New Kijang Innova menurun rata-rata 2.23/bulan, New Fortuner menurun rata-rata 2.23/bulan, All New Hilux menurun rata-rata 0,18/bulan, Hilux menurun rata-rata 0,14/bulan, dan New Hilux meningkat rata-rata 0,08/bulan.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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