Analisis Kualitas Pelayanan Jasa Angkutan Petikemas Di Pelabuhan Trisakti Banjarmasin
Bibliographic record
Abstract
Pelabuhan Trisakti merupakan pelabuhan utama di wilayah Kalimantan Selatan, dimana transportasi laut sangat diandalkan sebagai transportasi utama antar negara untuk melayani lalu lintas penumpang maupun barang. Arus petikemas dari tahun ke tahun mengalami peningkatan yang signifikan, pada tahun 2012 arus petikemas mencapai 384.323 box atau 419.335 TEU’s, dan pada tahun 2013 mengalami peningkatan menjadi 387.954 box atau 428.478 TEU’s. Maksud penelitian ini adalah mengevaluasi kondisi saat ini tentang kegiatan arus bongkar muat petikemas di TPKB dan memberikan gambaran serta upaya yang perlu dilakukan dalam rangka meningkatkan kualitas pelayanan jasa angkutan petikemas di TPKB. Tujuannya untuk mengetahui faktor-faktor penyebab kurangnya kualitas pelayanan jasa angkutan petikemas. Cara pengambilan sampel dilaksanakan dengan wawancara dan kuesioner kepada pengguna jasa angkutan petikemas dan SDM/operator di Terminal Petikemas Banjarmasin (TPKB) sebanyak 48 responden. Dengan menggunakan metode analisis IPA, maka hasil analisis perhitungan CSI dari 48 responden di TPKB se bernilai 66,410%, angka ini dikategorikan bahwa kualitas pelayanan jasa angkutan petikemas di TPKB belum memuaskan (Poor) untuk itu masih perlu ditingkatkan.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".