PENERAPAN JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI PENJUALAN MOBIL DENGAN MENGGUNAKAN METODE BACKPROPAGATION (Studi Kasus : Toyota Auto 2000 Medan)
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.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".