Perkiraan Tarikan Pergerakan Kendaraan Logistik Menuju ke Pulau Seram di Provinsi Maluku
Bibliographic record
Abstract
Transportasi logistik hingga kini masih menjadi masalah serius, terutama di wilayah kepulauan seperti Provinsi Maluku. Hal itu dapat tergambar dari tingginya harga barang akibat dari tingginya biaya logistik. Oleh sebab itu diperlukan dukungan informasi tentang besaran tarikan pergerakan kendaraan sebagai dasar penataan sistem transportasi logistik. Tujuan penelitian ini adalah mendeskripsikan sistem logistik dan memodelkan tarikan pergerakan kendaraan logistik menuju ke Pulau Seram di Provinsi Maluku. Variabel yang dimodelkan adalah jumlah penduduk (X1) dan luas wilayah (X2). Data dikumpulkan dengan metode wawancara terhadap 120 distributor. Hasil penelitian menemukan sistem logistik di wilayah Provinsi Maluku terdiri dari koridor utara yang berpusat di Kota Ambon dan koridor selatan dengan sistem multiport (Tiakur, Saumlaki dan Tual). Pada jalur distribusi ke pulau Seram, terdapat 7 zona tarikan, dominan menuju ke zona Masohi dan Bula. Hasil pemodelan menunjukkan variabel jumlah penduduk berpengaruh signifikan terhadap tarikan pergerakan. Sedangkan variabel luas wilayah tidak berpengaruh signifikan. Model yang dihasilkan yaitu Yke P.Seram = -14,92491 + 0,0009809 Xjum.pend – 0,0003799 Xluas wil. Model tersebut memperkirakan setiap penambahan 20.000 penduduk, akan menarik 5 kendaraan logistik per hari. Temuan ini bermanfaat sebagai dasar penataan sistem transportasi logistik di wilayah Provinsi Maluku.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".