ANALISIS KEPUASAN IMPORTIR BUAH (APEL,PIR DAN JERUK) \nTERHADAP PELAYANAN PT. TERMINAL PETIKEMAS SURABAYA
Bibliographic record
Abstract
International trade is generally carried out through the port because it is \nalways in large numbers to supply the people needed. Tanjung Perak Harbor is the \nlargest port in East Java, managed by PT. Terminal Petikemas Surabaya. The highest \nvolume of fruit imports are apples, oranges and pears from various countries such as \nChina, the United States, and Canada. Services of PT. Terminal Petikemas Surabaya \nhas a big effect on service users or consumers, in this case is the fruit importer. The \ntime proceed to entry of commodities at the port affects the stability of prices at the \nconsumer level. \nThe purpose of this study was to determine the satisfaction of importers with \nthe services of PT. Surabaya Container Terminal and analyzed the effect of service \nquality on the satisfaction of importers of apples, oranges and pears. There are 50 \ncompanies selected using the Purposive Random Sampling method. While the \nanalysis uses Multiple Linear Regression. \nThe results showed that there were 4 (four factors) which had a significant \neffect on importer satisfaction, Such as responsiveness, empathy, assurance and \nphysical evidence. The reliability factor does not significantly influence the \nsatisfaction of the importer. All the 5 (five) independent variables that affect 68.5% of \nimporter satisfaction. As many as 31.5% of other factors were not included in this \nstudy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".