Analisis Kinerja Bongkar Muat Kapal yang Mengalami Perpanjangan Masa Tambat Di Terminal Multipurpose PT Pelabuhan Tanjung Priok
Bibliographic record
Abstract
Kinerja bongkar muat kapal memiliki peran yang sangat besar dalam menentukan baik atau buruknya performa suatu pelabuhan. Tujuan analisis kinerja bongkar muat kapal yang mengalami perpanjangan masa tambat yaitu untuk melihat faktor apa saja yang mempengaruhi kegiatan bongkar muat, mengetahui tingkat pelayanan waktu tambat, dan menilai produktivitas kegiatan bongkar muat kapal. Metode penelitian yang digunakan dalam penyusunan ini adalah pendekatan metode kualitatif dan kuantitatif. Hasil penelitian dapat disimpulkan selama periode bulan januari sampai dengan bulan maret 2021 terdapat 93 kapal yang mengalami perpanjangan masa tambat dengan berbagai faktor yaitu; Clearence Out, Crane Trouble, Tenaga Kerja Bongkar Muat (TKBM), Waiting Truck, Weather, Handling Cargo. Hasil perhitungan rata-rata tingkat pelayanan waktu tambat kapal yang mengalami perpanjangan masa tambat berdasarkan perhitungan Effective Time dibanding dengan Berthing Time yaitu sebesar 66.15% dibawah dari standar KSOP yaitu 70%. Hasil perhitungan produktivitas bongkar muat kapal yang mengalami perpanjangan masa tambat menunjukkan nilai yang cukup baik.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".