FAKTOR EKSTERNAL DAN INTERNAL PERILAKU KESELAMATAN BERKENDARA PEKERJA KANTORAN PENGGUNA SEPEDA MOTOR (Wilayah Studi: Kota Tangerang Selatan)
Bibliographic record
Abstract
Kecelakaan lalu lintas yang terjadi di kawasan perkotaan terus meningkat, seiring dengan terus meningkatnya jumlah kendaraan di jalanan. Kecelakaan lalu lintas yang terjadi di Indonesia, sebagian besar disebabkan oleh faktor manusia. Salah satu upaya untuk mengatasi kecelakaan lalu lintas berdasarkan faktor manusia, dapat dilakukan dengan menganalisis perilaku pengguna jalan. Penelitian ini menggunakan metode structural equation modelling (SEM) untuk mengidentifikasi seberapa besar pengaruh faktor eksternal dan internal terhadap perilaku keselamatan berkendara pada pekerja kantoran pengguna sepeda motor di Kota Tangerang Selatan. Faktor Eksternal dilihat dari Theory of Planned Behavior, jarak tempuh, dan waktu tempuh perjalanan. Faktor internal dilihat dari trait kepribadian ekstraversi, kooperatif, neurotisme, dan kesadaran. Analisis dilakukan terhadap masing-masing golongan darah, sehingga dapat melihat faktor yang dominan. Berdasarkan hasil analisis perilaku keselamatan berkendara golongan darah A, B, dan AB lebih dipengaruhi oleh variabel dari faktor eksternal, sedangkan golongan darah O lebih dipengaruhi oleh variabel dari faktor internal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".