Prediksi Kebutuhan Kapasitas Dermaga Berdasarkan Tren Perubahan Pola Penyeberangan
Bibliographic record
Abstract
Penelitian ini mengkaji kebutuhan kapasitas dermaga pelabuhan penyeberangan di Pelabuhan Pomako, Papua dengan mempertimbangkan perubahan pola penyeberangan. Permasalahan utama adalah ketidakseimbangan antara kapasitas kapal dan fluktuasi permintaan penumpang serta kendaraan, yang berdampak pada operasional pelabuan. Metode yang digunakan adalah proyeksi ekonometrik berbasis analisis regresi menggunakan data time-series pada rentang tahun 2017–2021, yang mengaitkan jumlah pengguna pelabuhan, baik itu jumlah penumpang, motor serta angkutan ringan dan berat dengan variabel ekonomi dan demografi seperti PDRB dan jumlah penduduk. Hasil proyeksi menunjukkan peningkatan signifikan kebutuhan kapasitas dermaga seiring dengan pertumbuhan ekonomi dan populasi, dengan proyeksi peningkatan jumlah penumpang hingga 36,6% dan kendaraan angkutan ringan dan berat masing-masing hingga 57,7% dan 35,5% pada tahun 2030. Hasil penelitian menyimpulkan kapasitas dermaga saat ini perlu ditingkatkan untuk mengakomodasi lonjakan permintaan dan mengoptimalkan pelayanan pelabuhan agar efisien dan berkelanjutan. Rekomendasi ini penting untuk mendukung kelancaran mobilitas dan distribusi barang di wilayah Papua. This study examines the dock capacity requirements at Pomako Ferry ports, considering changing crossing patterns. The primary issue is the imbalance between vessel capacity and the fluctuating demand for passengers and vehicles, which impacts port operations. The method used is an econometric projection based on regression analysis using time-series data from 2017 to 2021, linking the number of port users—including passengers, motorcycles, and light and heavy vehicles—with economic and demographic variables such as GRDP and population. The projection results indicate a significant increase in dock capacity needs in line with economic and population growth, with passenger numbers expected to rise by 36.6%, and light and heavy vehicle transport by 57.7% and 35.5%, respectively, by 2030. The study concludes that the current dock capacity must be enhanced to accommodate demand surges and optimize port services for efficiency and sustainability. These recommendations are crucial to support smooth mobility and goods distribution in the Papua region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".